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1504.00325 | 16 | TABLE 1: Human Agreement for Image Captioning: Various metrics when benchmarking a human generated caption against ground truth captions.
Metric Name MS COCO c5 MS COCO c40 BLEU 1 BLEU 2 BLEU 3 BLEU 4 0.663 0.469 0.321 0.217 0.880 0.744 0.603 0.471 METEOR ROUGEL CIDEr-D 0.252 0.484 0.854 0.335 0.626 0.910
\ 10 =(ei) =U sig)? CIDEr-D,, (¢;, S;) = Tn DS e302 & j min(gâ (ci), 9â (siz)) 9" (Sis) llaâ (ea)MIlloâ (sis) Il » (19)
Where l(ci) and l(sij) denote the lengths of candidate and reference sentences respectively. Ï = 6 is used. A factor of 10 is used in the numerator to make the CIDEr- D scores numerically similar to the other metrics.
The ï¬nal CIDEr-D metric is computed in a similar manner to CIDEr (analogous to eqn. 18): | 1504.00325#16 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 16,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "TABLE 1: Human Agreement for Image Captioning: Various metrics when benchmarking a human generated caption against ground truth captions.\nMetric Name MS COCO c5 MS COCO c40 BLEU 1 BLEU 2 BLEU 3 BLEU 4 0.663 0.469 0.321 0.217 0.880 0.744 0.603 0.471 METEOR ROUGEL CIDEr-D 0.252 0.484 0.854 0.335 0.626 0.910\n\\ 10 =(ei) =U sig)? CIDEr-D,, (¢;, S;) = Tn DS e302 & j min(gâ (ci), 9â (siz)) 9\" (Sis) llaâ (ea)MIlloâ (sis) Il » (19)\nWhere l(ci) and l(sij) denote the lengths of candidate and reference sentences respectively. Ï = 6 is used. A factor of 10 is used in the numerator to make the CIDEr- D scores numerically similar to the other metrics.\nThe ï¬nal CIDEr-D metric is computed in a similar manner to CIDEr (analogous to eqn. 18):",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
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] |
1504.00325 | 17 | The ï¬nal CIDEr-D metric is computed in a similar manner to CIDEr (analogous to eqn. 18):
N CIDEr-D(;, 5:) = }> waCIDEr-D, (ci, $i), n=1 (20)
Note that just like the BLEU and ROUGE metrics, CIDEr- D does not use stemming. We adopt the CIDEr-D metric for the evaluation server.
4 HUMAN PERFORMANCE In this section, we study the human agreement among humans at this task. We start with analyzing the inter- human agreement for image captioning (Section. 4.1) and then analyze human agreement for the word prediction sub-task and provide a simple model which explains human agreement for this sub-task (Section. 4.2).
# 4.1 Human Agreement for Image Captioning
When examining human agreement on captions, it be- comes clear that there are many equivalent ways to say essentially the same thing. We quantify this by conducting the following experiment: We collect one additional human caption for each image in the test set and treat this caption as the prediction. Using the MS COCO caption evaluation server we compute the various metrics. The results are tabulated in Table 1.
# 4.2 Human Agreement for Word Prediction | 1504.00325#17 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 17,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "The ï¬nal CIDEr-D metric is computed in a similar manner to CIDEr (analogous to eqn. 18):\nN CIDEr-D(;, 5:) = }> waCIDEr-D, (ci, $i), n=1 (20)\nNote that just like the BLEU and ROUGE metrics, CIDEr- D does not use stemming. We adopt the CIDEr-D metric for the evaluation server.\n4 HUMAN PERFORMANCE In this section, we study the human agreement among humans at this task. We start with analyzing the inter- human agreement for image captioning (Section. 4.1) and then analyze human agreement for the word prediction sub-task and provide a simple model which explains human agreement for this sub-task (Section. 4.2).\n# 4.1 Human Agreement for Image Captioning\nWhen examining human agreement on captions, it be- comes clear that there are many equivalent ways to say essentially the same thing. We quantify this by conducting the following experiment: We collect one additional human caption for each image in the test set and treat this caption as the prediction. Using the MS COCO caption evaluation server we compute the various metrics. The results are tabulated in Table 1.\n# 4.2 Human Agreement for Word Prediction",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
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] |
1504.00325 | 18 | # 4.2 Human Agreement for Word Prediction
We can do a similar analysis for human agreement at the sub-task of word prediction. Consider the task of tagging the image with words that occur in the captions. For this task, we can compute the human precision and recall for
TABLE 2: Model deï¬ntions.
# o w n k q p
object or visual concept = = word associated with o total number of images = number of captions per image = P (o = 1) = P (w = 1|o = 1) =
a given word w by benchmarking words used in the k+1 human caption with respect to words used in the ï¬rst k reference captions. Note that we use weighted versions of precision and recall, where each negative image has a weight of 1 and each positive image has a weight equal to the number of captions containing the word w. Human precision (Hp) and human recall (Hr) can be computed from the counts of how many subjects out of k use the word w to describe a given image over the whole dataset. | 1504.00325#18 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 18,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "# 4.2 Human Agreement for Word Prediction\nWe can do a similar analysis for human agreement at the sub-task of word prediction. Consider the task of tagging the image with words that occur in the captions. For this task, we can compute the human precision and recall for\nTABLE 2: Model deï¬ntions.\n# o w n k q p\nobject or visual concept = = word associated with o total number of images = number of captions per image = P (o = 1) = P (w = 1|o = 1) =\na given word w by benchmarking words used in the k+1 human caption with respect to words used in the ï¬rst k reference captions. Note that we use weighted versions of precision and recall, where each negative image has a weight of 1 and each positive image has a weight equal to the number of captions containing the word w. Human precision (Hp) and human recall (Hr) can be computed from the counts of how many subjects out of k use the word w to describe a given image over the whole dataset.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 19 | We plot Hp versus Hr for a set of nouns, verbs and adjectives, and all 1000 words considered in Figure 3. Nouns referring to animals like âelephantâ have a high recall, which means that if an âelephantâ exists in the image, a subject is likely to talk about it (which makes intuitive sense, given âelephantâ images are somewhat rare, and there are no alternative words that could be used instead of âelephantâ). On the other hand, an adjective like âbrightâ is used inconsistently and hence has low recall. Interestingly, words with high recall also have high precision. Indeed, all the points of human agreement appear to lie on a one-dimensional curve in the two-dimension precision-recall space. | 1504.00325#19 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 19,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "We plot Hp versus Hr for a set of nouns, verbs and adjectives, and all 1000 words considered in Figure 3. Nouns referring to animals like âelephantâ have a high recall, which means that if an âelephantâ exists in the image, a subject is likely to talk about it (which makes intuitive sense, given âelephantâ images are somewhat rare, and there are no alternative words that could be used instead of âelephantâ). On the other hand, an adjective like âbrightâ is used inconsistently and hence has low recall. Interestingly, words with high recall also have high precision. Indeed, all the points of human agreement appear to lie on a one-dimensional curve in the two-dimension precision-recall space.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 20 | This observation motivates us to propose a simple model for when subjects use a particular word w for describing an image. Let o denote an object or visual concept associated with word w, n be the total number of images, and k be the number of reference captions. Next, let q = P (o = 1) be the probability that object o exists in an image. For clarity these deï¬nitions are summarized in Table 2. We make two simpliï¬cations. First, we ig- nore image level saliency and instead focus on word level saliency. Speciï¬cally, we only model p = P (w = 1|o = 1), the probability a subject uses w given that o is in the image, without conditioning on the image itself. Second, we assume that P (w = 1|o = 0) = 0, i.e. that a subject does not use w unless o is in the image. As we will show, even with these simpliï¬cations our model sufï¬ces to explain the empirical observations in Figure 3 to a reasonable degree of accuracy. | 1504.00325#20 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 20,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "This observation motivates us to propose a simple model for when subjects use a particular word w for describing an image. Let o denote an object or visual concept associated with word w, n be the total number of images, and k be the number of reference captions. Next, let q = P (o = 1) be the probability that object o exists in an image. For clarity these deï¬nitions are summarized in Table 2. We make two simpliï¬cations. First, we ig- nore image level saliency and instead focus on word level saliency. Speciï¬cally, we only model p = P (w = 1|o = 1), the probability a subject uses w given that o is in the image, without conditioning on the image itself. Second, we assume that P (w = 1|o = 0) = 0, i.e. that a subject does not use w unless o is in the image. As we will show, even with these simpliï¬cations our model sufï¬ces to explain the empirical observations in Figure 3 to a reasonable degree of accuracy.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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1504.00325 | 21 | Given these assumptions, we can model human preci- sion Hy and recall H, for a word w given only p and k. First, given k captions per image, we need to compute the expected number of (1) captions containing w (cw), (2) true positives (tp), and (3) false positives (fp). Note that in our definition there can be up to k true positives per image (if cw = k, i.e. each of the k captions contains word w) but at most 1 false positive (if none of the k captions contains w). The expectations, in terms of k, p,
and q are: | 1504.00325#21 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 21,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "Given these assumptions, we can model human preci- sion Hy and recall H, for a word w given only p and k. First, given k captions per image, we need to compute the expected number of (1) captions containing w (cw), (2) true positives (tp), and (3) false positives (fp). Note that in our definition there can be up to k true positives per image (if cw = k, i.e. each of the k captions contains word w) but at most 1 false positive (if none of the k captions contains w). The expectations, in terms of k, p,\nand q are:",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 22 | and q are:
E[cw] = Σk i=1P (wi = 1) = ΣiP (wi = 1|o = 1)P (o = 1) +ΣiP (wi = 1|o = 0)P (o = 0) = kpq + 0 = kpq E[tp] = Σk i=1P (wi = 1 ⧠wk+1 = 1) = ΣiP (wi = 1 ⧠wk+1 = 1|o = 1)P (o = 1) +ΣiP (wi = 1 ⧠wk+1 = 1|o = 0)P (o = 0) = kppq + 0 = kp2q E[f p] = P (w1 . . . wk = 0 ⧠wk+1 = 1) = P (o = 1 ⧠w1 . . . wk = 0 ⧠wk+1 = 1) +P (o = 0 ⧠w1 . . . wk = 0 ⧠wk+1 = 1) = q(1 â p)kp + 0 = q(1 â p)kp | 1504.00325#22 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 22,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "and q are:\nE[cw] = Σk i=1P (wi = 1) = ΣiP (wi = 1|o = 1)P (o = 1) +ΣiP (wi = 1|o = 0)P (o = 0) = kpq + 0 = kpq E[tp] = Σk i=1P (wi = 1 ⧠wk+1 = 1) = ΣiP (wi = 1 ⧠wk+1 = 1|o = 1)P (o = 1) +ΣiP (wi = 1 ⧠wk+1 = 1|o = 0)P (o = 0) = kppq + 0 = kp2q E[f p] = P (w1 . . . wk = 0 ⧠wk+1 = 1) = P (o = 1 ⧠w1 . . . wk = 0 ⧠wk+1 = 1) +P (o = 0 ⧠w1 . . . wk = 0 ⧠wk+1 = 1) = q(1 â p)kp + 0 = q(1 â p)kp",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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1504.00325 | 23 | In the above wi = 1 denotes that w appeared in the ith caption. Note that we are also assuming independence between subjects conditioned on o. We can now deï¬ne model precision and recall as:
ir : nE p] pk » nEftp] + nE[ fp] ~ pk + (1 âp)é 7 nE[tp] _ Hy 3 nE|cu] P
Note that these expressions are independent of q and only depend on p. Interestingly, because of the use of weighted precision and recall, the recall for a category comes out to be exactly equal to p, the probability a subject uses w given that o is in the image. | 1504.00325#23 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 23,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "In the above wi = 1 denotes that w appeared in the ith caption. Note that we are also assuming independence between subjects conditioned on o. We can now deï¬ne model precision and recall as:\nir : nE\tp] pk » nEftp] + nE[ fp] ~ pk + (1 âp)é 7 nE[tp] _ Hy 3 nE|cu] P\nNote that these expressions are independent of q and only depend on p. Interestingly, because of the use of weighted precision and recall, the recall for a category comes out to be exactly equal to p, the probability a subject uses w given that o is in the image.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 24 | We set k = 4 and vary p to plot Hp versus H,, getting the curve as shown in blue in Figure 3 (bottom left). The curve explains the observed data quite well, closely matching the precision-recall tradeoffs of the empirical data (although not perfectly). We can also reduce the number of captions from four, and look at how the empirical and predicted precision and recall change. Figure 3 (bottom right), shows this variation as we reduce the number of reference captions per image from four to one annotations. We see that the points of human agreement remain at the same recall value, but decrease in their precision, which is consistent with what the model predicts. Also, the human precision at infinite subjects will approach one, which is again reasonable given that a subject will only use the word w if the corresponding object is in the image (and in the presence of infinite subjects someone else will also use the word w).
In fact, the ï¬xed recall value can help us recover p, the probability that a subject will use the word w in describing the image given the object is present. Nouns like âelephantâ and âtennisâ have large p, which is reasonable. Verbs and adjectives, on the other hand, have smaller p values, which can be justiï¬ed from the fact that a) subjects are less likely to describe attributes
5
6 | 1504.00325#24 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 24,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "We set k = 4 and vary p to plot Hp versus H,, getting the curve as shown in blue in Figure 3 (bottom left). The curve explains the observed data quite well, closely matching the precision-recall tradeoffs of the empirical data (although not perfectly). We can also reduce the number of captions from four, and look at how the empirical and predicted precision and recall change. Figure 3 (bottom right), shows this variation as we reduce the number of reference captions per image from four to one annotations. We see that the points of human agreement remain at the same recall value, but decrease in their precision, which is consistent with what the model predicts. Also, the human precision at infinite subjects will approach one, which is again reasonable given that a subject will only use the word w if the corresponding object is in the image (and in the presence of infinite subjects someone else will also use the word w).\nIn fact, the ï¬xed recall value can help us recover p, the probability that a subject will use the word w in describing the image given the object is present. Nouns like âelephantâ and âtennisâ have large p, which is reasonable. Verbs and adjectives, on the other hand, have smaller p values, which can be justiï¬ed from the fact that a) subjects are less likely to describe attributes\n5\n6",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 25 | 5
6
Nouns boy ote Precision Precision Adjectives Precision Recall Precision Recall Recall "Recall Precision ânumber of reference = 1 Recall
Fig. 3: Precision-recall points for human agreement: we compute precision and recall by treating one human caption as prediction and benchmark it against the others to obtain points on the precision recall curve. We plot these points for example nouns (top left), adjectives (top center), and verbs (top right), and for all words (bottom left). We also plot the ï¬t of our model for human agreement with the empirical data (bottom left) and show how the human agreement changes with different number of captions being used (bottom right). We see that the human agreement point remains at the same recall value but dips in precision when using fewer captions.
of objects and b) subjects might use a different word (synonym) to describe the same attribute.
This analysis of human agreement also motivates us- ing a different metric for measuring performance. We propose Precision at Human Recall (PHR) as a metric for measuring performance of a vision system perform- ing this task. Given that human recall for a particular word is ï¬xed and precision varies with the number of annotations, we can look at system precision at human recall and compare it with human precision to report the performance of the vision system. | 1504.00325#25 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 25,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "5\n6\nNouns boy ote Precision Precision Adjectives Precision Recall Precision Recall Recall \"Recall Precision ânumber of reference = 1 Recall\nFig. 3: Precision-recall points for human agreement: we compute precision and recall by treating one human caption as prediction and benchmark it against the others to obtain points on the precision recall curve. We plot these points for example nouns (top left), adjectives (top center), and verbs (top right), and for all words (bottom left). We also plot the ï¬t of our model for human agreement with the empirical data (bottom left) and show how the human agreement changes with different number of captions being used (bottom right). We see that the human agreement point remains at the same recall value but dips in precision when using fewer captions.\nof objects and b) subjects might use a different word (synonym) to describe the same attribute.\nThis analysis of human agreement also motivates us- ing a different metric for measuring performance. We propose Precision at Human Recall (PHR) as a metric for measuring performance of a vision system perform- ing this task. Given that human recall for a particular word is ï¬xed and precision varies with the number of annotations, we can look at system precision at human recall and compare it with human precision to report the performance of the vision system.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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1504.00325 | 26 | 5 EVALUATION SERVER INSTRUCTIONS Directions on how to use the MS COCO caption evalu- ation server can be found on the MS COCO website. The evaluation server is hosted by CodaLab. To par- ticipate, a user account on CodaLab must be created. The participants need to generate results on both the validation and testing datasets. When training for the generation of results on the test dataset, the training and validation dataset may be used as the participant sees ï¬t. That is, the validation dataset may be used for training if desired. However, when generating results on the validation set, we ask participants to only train on the training dataset, and only use the validation dataset
for tuning meta-parameters. Two JSON ï¬les should be created corresponding to results on each dataset in the following format:
[{ âimage idâ âcaptionâ }] : : int, str,
The results may then be placed into a zip ï¬le and uploaded to the server for evaluation. Code is also provided on GitHub to evaluate results on the validation dataset without having to upload to the server. The number of submissions per user is limited to a ï¬xed amount.
# 6 DISCUSSION | 1504.00325#26 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 26,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "5 EVALUATION SERVER INSTRUCTIONS Directions on how to use the MS COCO caption evalu- ation server can be found on the MS COCO website. The evaluation server is hosted by CodaLab. To par- ticipate, a user account on CodaLab must be created. The participants need to generate results on both the validation and testing datasets. When training for the generation of results on the test dataset, the training and validation dataset may be used as the participant sees ï¬t. That is, the validation dataset may be used for training if desired. However, when generating results on the validation set, we ask participants to only train on the training dataset, and only use the validation dataset\nfor tuning meta-parameters. Two JSON ï¬les should be created corresponding to results on each dataset in the following format:\n[{ âimage idâ âcaptionâ }] : : int, str,\nThe results may then be placed into a zip ï¬le and uploaded to the server for evaluation. Code is also provided on GitHub to evaluate results on the validation dataset without having to upload to the server. The number of submissions per user is limited to a ï¬xed amount.\n# 6 DISCUSSION",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 27 | # 6 DISCUSSION
Many challenges exist when creating an image caption dataset. As stated in [7], [42], [45] the captions generated by human subjects can vary signiï¬cantly. However even though two captions may be very different, they may be judged equally âgoodâ by human subjects. Designing effective automatic evaluation metrics that are highly correlated with human judgment remains a difï¬cult challenge [7], [42], [45], [46]. We hope that by releasing
results on the validation data, we can help enable future research in this area.
Since automatic evaluation metrics do not always correspond to human judgment, we hope to conduct experiments using human subjects to judge the quality of automatically generated captions, which are most similar to human captions, and whether they are grammatically correct [45], [42], [7], [4], [5]. This is essential to determin- ing whether future algorithms are indeed improving, or whether they are merely over ï¬tting to a speciï¬c metric. These human experiments will also allow us to evaluate the automatic evaluation metrics themselves, and see which ones are correlated to human judgment.
# REFERENCES
[1] K. Barnard and D. Forsyth, âLearning the semantics of words and pictures,â in ICCV, vol. 2, 2001, pp. 408â415. | 1504.00325#27 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 27,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "# 6 DISCUSSION\nMany challenges exist when creating an image caption dataset. As stated in [7], [42], [45] the captions generated by human subjects can vary signiï¬cantly. However even though two captions may be very different, they may be judged equally âgoodâ by human subjects. Designing effective automatic evaluation metrics that are highly correlated with human judgment remains a difï¬cult challenge [7], [42], [45], [46]. We hope that by releasing\nresults on the validation data, we can help enable future research in this area.\nSince automatic evaluation metrics do not always correspond to human judgment, we hope to conduct experiments using human subjects to judge the quality of automatically generated captions, which are most similar to human captions, and whether they are grammatically correct [45], [42], [7], [4], [5]. This is essential to determin- ing whether future algorithms are indeed improving, or whether they are merely over ï¬tting to a speciï¬c metric. These human experiments will also allow us to evaluate the automatic evaluation metrics themselves, and see which ones are correlated to human judgment.\n# REFERENCES\n[1] K. Barnard and D. Forsyth, âLearning the semantics of words and pictures,â in ICCV, vol. 2, 2001, pp. 408â415.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 28 | [2] K. Barnard, P. Duygulu, D. Forsyth, N. De Freitas, D. M. Blei, and M. I. Jordan, âMatching words and pictures,â JMLR, vol. 3, pp. 1107â1135, 2003.
[3] V. Lavrenko, R. Manmatha, and J. Jeon, âA model for learning the semantics of pictures,â in NIPS, 2003.
[4] G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. C. Berg, and T. L. Berg, âBaby talk: Understanding and generating simple image descriptions,â in CVPR, 2011.
[5] M. Mitchell, X. Han, J. Dodge, A. Mensch, A. Goyal, A. Berg, K. Yamaguchi, T. Berg, K. Stratos, and H. Daum´e III, âMidge: Generating image descriptions from computer vision detections,â in EACL, 2012. | 1504.00325#28 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 28,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "[2] K. Barnard, P. Duygulu, D. Forsyth, N. De Freitas, D. M. Blei, and M. I. Jordan, âMatching words and pictures,â JMLR, vol. 3, pp. 1107â1135, 2003.\n[3] V. Lavrenko, R. Manmatha, and J. Jeon, âA model for learning the semantics of pictures,â in NIPS, 2003.\n[4] G. Kulkarni, V. Premraj, S. Dhar, S. Li, Y. Choi, A. C. Berg, and T. L. Berg, âBaby talk: Understanding and generating simple image descriptions,â in CVPR, 2011.\n[5] M. Mitchell, X. Han, J. Dodge, A. Mensch, A. Goyal, A. Berg, K. Yamaguchi, T. Berg, K. Stratos, and H. Daum´e III, âMidge: Generating image descriptions from computer vision detections,â in EACL, 2012.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 29 | [6] A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth, âEvery picture tells a story: Generating sentences from images,â in ECCV, 2010.
[7] M. Hodosh, P. Young, and J. Hockenmaier, âFraming image de- scription as a ranking task: Data, models and evaluation metrics.â JAIR, vol. 47, pp. 853â899, 2013.
[8] P. Kuznetsova, V. Ordonez, A. C. Berg, T. L. Berg, and Y. Choi, âCollective generation of natural image descriptions,â in ACL, 2012.
[9] Y. Yang, C. L. Teo, H. Daum´e III, and Y. Aloimonos, âCorpus- guided sentence generation of natural images,â in EMNLP, 2011. [10] A. Gupta, Y. Verma, and C. Jawahar, âChoosing linguistics over
vision to describe images.â in AAAI, 2012. | 1504.00325#29 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 29,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "[6] A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth, âEvery picture tells a story: Generating sentences from images,â in ECCV, 2010.\n[7] M. Hodosh, P. Young, and J. Hockenmaier, âFraming image de- scription as a ranking task: Data, models and evaluation metrics.â JAIR, vol. 47, pp. 853â899, 2013.\n[8] P. Kuznetsova, V. Ordonez, A. C. Berg, T. L. Berg, and Y. Choi, âCollective generation of natural image descriptions,â in ACL, 2012.\n[9] Y. Yang, C. L. Teo, H. Daum´e III, and Y. Aloimonos, âCorpus- guided sentence generation of natural images,â in EMNLP, 2011. [10] A. Gupta, Y. Verma, and C. Jawahar, âChoosing linguistics over\nvision to describe images.â in AAAI, 2012.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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1504.00325 | 30 | vision to describe images.â in AAAI, 2012.
[11] E. Bruni, G. Boleda, M. Baroni, and N.-K. Tran, âDistributional semantics in technicolor,â in ACL, 2012.
[12] Y. Feng and M. Lapata, âAutomatic caption generation for news images,â TPAMI, vol. 35, no. 4, pp. 797â812, 2013.
[13] D. Elliott and F. Keller, âImage description using visual depen- dency representations.â in EMNLP, 2013, pp. 1292â1302.
[14] A. Karpathy, A. Joulin, and F.-F. Li, âDeep fragment embeddings for bidirectional image sentence mapping,â in NIPS, 2014. [15] Y. Gong, L. Wang, M. Hodosh, J. Hockenmaier, and S. Lazebnik, âImproving image-sentence embeddings using large weakly an- notated photo collections,â in ECCV, 2014, pp. 529â545.
[16] R. Mason and E. Charniak, âNonparametric method for data- driven image captioning,â in ACL, 2014. | 1504.00325#30 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 30,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "vision to describe images.â in AAAI, 2012.\n[11] E. Bruni, G. Boleda, M. Baroni, and N.-K. Tran, âDistributional semantics in technicolor,â in ACL, 2012.\n[12] Y. Feng and M. Lapata, âAutomatic caption generation for news images,â TPAMI, vol. 35, no. 4, pp. 797â812, 2013.\n[13] D. Elliott and F. Keller, âImage description using visual depen- dency representations.â in EMNLP, 2013, pp. 1292â1302.\n[14] A. Karpathy, A. Joulin, and F.-F. Li, âDeep fragment embeddings for bidirectional image sentence mapping,â in NIPS, 2014. [15] Y. Gong, L. Wang, M. Hodosh, J. Hockenmaier, and S. Lazebnik, âImproving image-sentence embeddings using large weakly an- notated photo collections,â in ECCV, 2014, pp. 529â545.\n[16] R. Mason and E. Charniak, âNonparametric method for data- driven image captioning,â in ACL, 2014.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 31 | [16] R. Mason and E. Charniak, âNonparametric method for data- driven image captioning,â in ACL, 2014.
[17] P. Kuznetsova, V. Ordonez, T. Berg, and Y. Choi, âTreetalk: Com- position and compression of trees for image descriptions,â TACL, vol. 2, pp. 351â362, 2014.
[18] K. Ramnath, S. Baker, L. Vanderwende, M. El-Saban, S. N. Sinha, A. Kannan, N. Hassan, M. Galley, Y. Yang, D. Ramanan, A. Bergamo, and L. Torresani, âAutocaption: Automatic caption generation for personal photos,â in WACV, 2014.
[19] A. Lazaridou, E. Bruni, and M. Baroni, âIs this a wampimuk? cross-modal mapping between distributional semantics and the visual world,â in ACL, 2014.
[20] R. Kiros, R. Salakhutdinov, and R. Zemel, âMultimodal neural language models,â in ICML, 2014. | 1504.00325#31 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 31,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "[16] R. Mason and E. Charniak, âNonparametric method for data- driven image captioning,â in ACL, 2014.\n[17] P. Kuznetsova, V. Ordonez, T. Berg, and Y. Choi, âTreetalk: Com- position and compression of trees for image descriptions,â TACL, vol. 2, pp. 351â362, 2014.\n[18] K. Ramnath, S. Baker, L. Vanderwende, M. El-Saban, S. N. Sinha, A. Kannan, N. Hassan, M. Galley, Y. Yang, D. Ramanan, A. Bergamo, and L. Torresani, âAutocaption: Automatic caption generation for personal photos,â in WACV, 2014.\n[19] A. Lazaridou, E. Bruni, and M. Baroni, âIs this a wampimuk? cross-modal mapping between distributional semantics and the visual world,â in ACL, 2014.\n[20] R. Kiros, R. Salakhutdinov, and R. Zemel, âMultimodal neural language models,â in ICML, 2014.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 32 | [21] J. Mao, W. Xu, Y. Yang, J. Wang, and A. L. Yuille, âExplain im- ages with multimodal recurrent neural networks,â arXiv preprint arXiv:1410.1090, 2014.
[22] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, âShow and tell: A neural image caption generator,â arXiv preprint arXiv:1411.4555, 2014.
[23] A. Karpathy and L. Fei-Fei, âDeep visual-semantic alignments for generating image descriptions,â arXiv preprint arXiv:1412.2306, 2014.
[24] R. Kiros, R. Salakhutdinov, and R. S. Zemel, âUnifying visual- semantic embeddings with multimodal neural language models,â arXiv preprint arXiv:1411.2539, 2014. | 1504.00325#32 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 32,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "[21] J. Mao, W. Xu, Y. Yang, J. Wang, and A. L. Yuille, âExplain im- ages with multimodal recurrent neural networks,â arXiv preprint arXiv:1410.1090, 2014.\n[22] O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, âShow and tell: A neural image caption generator,â arXiv preprint arXiv:1411.4555, 2014.\n[23] A. Karpathy and L. Fei-Fei, âDeep visual-semantic alignments for generating image descriptions,â arXiv preprint arXiv:1412.2306, 2014.\n[24] R. Kiros, R. Salakhutdinov, and R. S. Zemel, âUnifying visual- semantic embeddings with multimodal neural language models,â arXiv preprint arXiv:1411.2539, 2014.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
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] |
1504.00325 | 33 | [25] J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, âLong-term recurrent convolutional networks for visual recognition and description,â arXiv preprint arXiv:1411.4389, 2014.
[26] H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Doll´ar, J. Gao, X. He, M. Mitchell, J. Platt et al., âFrom captions to visual concepts and back,â arXiv preprint arXiv:1411.4952, 2014.
[27] X. Chen and C. L. Zitnick, âLearning a recurrent visual representa- tion for image caption generation,â arXiv preprint arXiv:1411.5654, 2014.
[28] R. Lebret, P. O. Pinheiro, and R. Collobert, âPhrase-based image captioning,â arXiv preprint arXiv:1502.03671, 2015. | 1504.00325#33 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 33,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "[25] J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, âLong-term recurrent convolutional networks for visual recognition and description,â arXiv preprint arXiv:1411.4389, 2014.\n[26] H. Fang, S. Gupta, F. Iandola, R. Srivastava, L. Deng, P. Doll´ar, J. Gao, X. He, M. Mitchell, J. Platt et al., âFrom captions to visual concepts and back,â arXiv preprint arXiv:1411.4952, 2014.\n[27] X. Chen and C. L. Zitnick, âLearning a recurrent visual representa- tion for image caption generation,â arXiv preprint arXiv:1411.5654, 2014.\n[28] R. Lebret, P. O. Pinheiro, and R. Collobert, âPhrase-based image captioning,â arXiv preprint arXiv:1502.03671, 2015.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
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1504.00325 | 34 | [29] ââ, âSimple image description generator via a linear phrase- based approach,â arXiv preprint arXiv:1412.8419, 2014.
[30] A. Lazaridou, N. T. Pham, and M. Baroni, âCombining language and vision with a multimodal skip-gram model,â arXiv preprint arXiv:1501.02598, 2015.
[31] A. Krizhevsky, I. Sutskever, and G. Hinton, âImageNet classiï¬ca- tion with deep convolutional neural networks,â in NIPS, 2012.
[32] S. Hochreiter and J. Schmidhuber, âLong short-term memory,â Neural computation, vol. 9, no. 8, pp. 1735â1780, 1997.
[33] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, âIm- ageNet: A Large-Scale Hierarchical Image Database,â in CVPR, 2009. | 1504.00325#34 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 34,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "[29] ââ, âSimple image description generator via a linear phrase- based approach,â arXiv preprint arXiv:1412.8419, 2014.\n[30] A. Lazaridou, N. T. Pham, and M. Baroni, âCombining language and vision with a multimodal skip-gram model,â arXiv preprint arXiv:1501.02598, 2015.\n[31] A. Krizhevsky, I. Sutskever, and G. Hinton, âImageNet classiï¬ca- tion with deep convolutional neural networks,â in NIPS, 2012.\n[32] S. Hochreiter and J. Schmidhuber, âLong short-term memory,â Neural computation, vol. 9, no. 8, pp. 1735â1780, 1997.\n[33] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, âIm- ageNet: A Large-Scale Hierarchical Image Database,â in CVPR, 2009.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 35 | [34] M. Grubinger, P. Clough, H. M ¨uller, and T. Deselaers, âThe iapr tc- 12 benchmark: A new evaluation resource for visual information systems,â in LREC Workshop on Language Resources for Content- based Image Retrieval, 2006.
[35] V. Ordonez, G. Kulkarni, and T. Berg, âIm2text: Describing images using 1 million captioned photographs.â in NIPS, 2011.
[36] P. Young, A. Lai, M. Hodosh, and J. Hockenmaier, âFrom image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,â TACL, vol. 2, pp. 67â 78, 2014.
[37] J. Chen, P. Kuznetsova, D. Warren, and Y. Choi, âD´ej´a image- captions: A corpus of expressive image descriptions in repetition,â in NAACL, 2015. | 1504.00325#35 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 35,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "[34] M. Grubinger, P. Clough, H. M ¨uller, and T. Deselaers, âThe iapr tc- 12 benchmark: A new evaluation resource for visual information systems,â in LREC Workshop on Language Resources for Content- based Image Retrieval, 2006.\n[35] V. Ordonez, G. Kulkarni, and T. Berg, âIm2text: Describing images using 1 million captioned photographs.â in NIPS, 2011.\n[36] P. Young, A. Lai, M. Hodosh, and J. Hockenmaier, âFrom image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,â TACL, vol. 2, pp. 67â 78, 2014.\n[37] J. Chen, P. Kuznetsova, D. Warren, and Y. Choi, âD´ej´a image- captions: A corpus of expressive image descriptions in repetition,â in NAACL, 2015.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 36 | [38] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar, and C. L. Zitnick, âMicrosoft COCO: Common objects in context,â in ECCV, 2014.
[39] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, âBleu: a method for automatic evaluation of machine translation,â in ACL, 2002.
[40] C.-Y. Lin, âRouge: A package for automatic evaluation of sum- maries,â in ACL Workshop, 2004.
[41] M. Denkowski and A. Lavie, âMeteor universal: Language spe- ciï¬c translation evaluation for any target language,â in EACL Workshop on Statistical Machine Translation, 2014.
âCider: Consensus-based image description evaluation,â arXiv preprint arXiv:1411.5726, 2014. | 1504.00325#36 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 36,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "[38] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar, and C. L. Zitnick, âMicrosoft COCO: Common objects in context,â in ECCV, 2014.\n[39] K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, âBleu: a method for automatic evaluation of machine translation,â in ACL, 2002.\n[40] C.-Y. Lin, âRouge: A package for automatic evaluation of sum- maries,â in ACL Workshop, 2004.\n[41] M. Denkowski and A. Lavie, âMeteor universal: Language spe- ciï¬c translation evaluation for any target language,â in EACL Workshop on Statistical Machine Translation, 2014.\nâCider: Consensus-based image description evaluation,â arXiv preprint arXiv:1411.5726, 2014.",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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] |
1504.00325 | 37 | âCider: Consensus-based image description evaluation,â arXiv preprint arXiv:1411.5726, 2014.
J. Bethard, and D. McClosky, âThe Stanford CoreNLP natural language processing toolkit,â in Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014, pp. 55â60. [Online]. Available: http: //www.aclweb.org/anthology/P/P14/P14-5010
[44] G. A. Miller, âWordnet: a lexical database for english,â Communi- cations of the ACM, vol. 38, no. 11, pp. 39â41, 1995.
[45] D. Elliott and F. Keller, âComparing automatic evaluation mea- sures for image description,â in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 2, 2014, pp. 452â457.
[46] C. Callison-Burch, M. Osborne, and P. Koehn, âRe-evaluation the role of bleu in machine translation research.â in EACL, vol. 6, 2006, pp. 249â256.
7 | 1504.00325#37 | Microsoft COCO Captions: Data Collection and Evaluation Server | In this paper we describe the Microsoft COCO Caption dataset and evaluation
server. When completed, the dataset will contain over one and a half million
captions describing over 330,000 images. For the training and validation
images, five independent human generated captions will be provided. To ensure
consistency in evaluation of automatic caption generation algorithms, an
evaluation server is used. The evaluation server receives candidate captions
and scores them using several popular metrics, including BLEU, METEOR, ROUGE
and CIDEr. Instructions for using the evaluation server are provided. | http://arxiv.org/pdf/1504.00325 | Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick | cs.CV, cs.CL | arXiv admin note: text overlap with arXiv:1411.4952 | null | cs.CV | 20150401 | 20150403 | [
{
"id": "1502.03671"
},
{
"id": "1501.02598"
}
] | {
"authors": "Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick",
"chunk_id": 37,
"doc_id": "1504.00325",
"primary_category": "cs.CV",
"published": 20150401,
"source": "http://arxiv.org/pdf/1504.00325",
"summary": "In this paper we describe the Microsoft COCO Caption dataset and evaluation\nserver. When completed, the dataset will contain over one and a half million\ncaptions describing over 330,000 images. For the training and validation\nimages, five independent human generated captions will be provided. To ensure\nconsistency in evaluation of automatic caption generation algorithms, an\nevaluation server is used. The evaluation server receives candidate captions\nand scores them using several popular metrics, including BLEU, METEOR, ROUGE\nand CIDEr. Instructions for using the evaluation server are provided.",
"text": "âCider: Consensus-based image description evaluation,â arXiv preprint arXiv:1411.5726, 2014.\nJ. Bethard, and D. McClosky, âThe Stanford CoreNLP natural language processing toolkit,â in Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014, pp. 55â60. [Online]. Available: http: //www.aclweb.org/anthology/P/P14/P14-5010\n[44] G. A. Miller, âWordnet: a lexical database for english,â Communi- cations of the ACM, vol. 38, no. 11, pp. 39â41, 1995.\n[45] D. Elliott and F. Keller, âComparing automatic evaluation mea- sures for image description,â in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 2, 2014, pp. 452â457.\n[46] C. Callison-Burch, M. Osborne, and P. Koehn, âRe-evaluation the role of bleu in machine translation research.â in EACL, vol. 6, 2006, pp. 249â256.\n7",
"title": "Microsoft COCO Captions: Data Collection and Evaluation Server",
"year": 2015
} | [
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1502.05477 | 0 | 7 1 0 2
r p A 0 2 ] G L . s c [
5 v 7 7 4 5 0 . 2 0 5 1 : v i X r a
# Trust Region Policy Optimization
John Schulman Sergey Levine Philipp Moritz Michael Jordan Pieter Abbeel University of California, Berkeley, Department of Electrical Engineering and Computer Sciences
[email protected] [email protected] [email protected] [email protected] [email protected]
# Abstract
We describe an iterative procedure for optimizing policies, with guaranteed monotonic improve- ment. By making several approximations to the theoretically-justiï¬ed procedure, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is similar to natural policy gradient methods and is effec- tive for optimizing large nonlinear policies such as neural networks. Our experiments demon- strate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. De- spite its approximations that deviate from the theory, TRPO tends to give monotonic improve- ment, with little tuning of hyperparameters. | 1502.05477#0 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 0,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "7 1 0 2\nr p A 0 2 ] G L . s c [\n5 v 7 7 4 5 0 . 2 0 5 1 : v i X r a\n# Trust Region Policy Optimization\nJohn Schulman Sergey Levine Philipp Moritz Michael Jordan Pieter Abbeel University of California, Berkeley, Department of Electrical Engineering and Computer Sciences\[email protected] [email protected] [email protected] [email protected] [email protected]\n# Abstract\nWe describe an iterative procedure for optimizing policies, with guaranteed monotonic improve- ment. By making several approximations to the theoretically-justiï¬ed procedure, we develop a practical algorithm, called Trust Region Policy Optimization (TRPO). This algorithm is similar to natural policy gradient methods and is effec- tive for optimizing large nonlinear policies such as neural networks. Our experiments demon- strate its robust performance on a wide variety of tasks: learning simulated robotic swimming, hopping, and walking gaits; and playing Atari games using images of the screen as input. De- spite its approximations that deviate from the theory, TRPO tends to give monotonic improve- ment, with little tuning of hyperparameters.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 0 | 5 1 0 2 c e D 1 3 ] I A . s c [ 0 1 v 8 9 6 5 0 . 2 0 5 1 : v i X r a
# Under review as a conference paper at ICLR 2016
TOWARDS AI-COMPLETE QUESTION ANSWERING: A SET OF PREREQUISITE TOY TASKS
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merri¨enboer, Armand Joulin & Tomas Mikolov Facebook AI Research 770 Broadway New York, USA {jase,abordes,spchopra,tmikolov,sashar,bartvm}@fb.com
# ABSTRACT | 1502.05698#0 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 0,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "5 1 0 2 c e D 1 3 ] I A . s c [ 0 1 v 8 9 6 5 0 . 2 0 5 1 : v i X r a\n# Under review as a conference paper at ICLR 2016\nTOWARDS AI-COMPLETE QUESTION ANSWERING: A SET OF PREREQUISITE TOY TASKS\nJason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merri¨enboer, Armand Joulin & Tomas Mikolov Facebook AI Research 770 Broadway New York, USA {jase,abordes,spchopra,tmikolov,sashar,bartvm}@fb.com\n# ABSTRACT",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 1 | Tetris is a classic benchmark problem for approximate dy- namic programming (ADP) methods, stochastic optimiza- tion methods are difï¬cult to beat on this task (Gabillon et al., 2013). For continuous control problems, methods like CMA have been successful at learning control poli- cies for challenging tasks like locomotion when provided with hand-engineered policy classes with low-dimensional parameterizations (Wampler & Popovi´c, 2009). The in- ability of ADP and gradient-based methods to consistently beat gradient-free random search is unsatisfying, since gradient-based optimization algorithms enjoy much better sample complexity guarantees than gradient-free methods (Nemirovski, 2005). Continuous gradient-based optimiza- tion has been very successful at learning function approxi- mators for supervised learning tasks with huge numbers of parameters, and extending their success to reinforcement learning would allow for efï¬cient training of complex and powerful policies.
# 1 Introduction | 1502.05477#1 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 1,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Tetris is a classic benchmark problem for approximate dy- namic programming (ADP) methods, stochastic optimiza- tion methods are difï¬cult to beat on this task (Gabillon et al., 2013). For continuous control problems, methods like CMA have been successful at learning control poli- cies for challenging tasks like locomotion when provided with hand-engineered policy classes with low-dimensional parameterizations (Wampler & Popovi´c, 2009). The in- ability of ADP and gradient-based methods to consistently beat gradient-free random search is unsatisfying, since gradient-based optimization algorithms enjoy much better sample complexity guarantees than gradient-free methods (Nemirovski, 2005). Continuous gradient-based optimiza- tion has been very successful at learning function approxi- mators for supervised learning tasks with huge numbers of parameters, and extending their success to reinforcement learning would allow for efï¬cient training of complex and powerful policies.\n# 1 Introduction",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 1 | # ABSTRACT
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the use- fulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.
1
# INTRODUCTION | 1502.05698#1 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 1,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "# ABSTRACT\nOne long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent. To measure progress towards that goal, we argue for the use- fulness of a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. We believe many existing learning systems can currently not solve them, and hence our aim is to classify these tasks into skill sets, so that researchers can identify (and then rectify) the failings of their systems. We also extend and improve the recently introduced Memory Networks model, and show it is able to solve some, but not all, of the tasks.\n1\n# INTRODUCTION",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 2 | # 1 Introduction
Most algorithms for policy optimization can be classiï¬ed into three broad categories: (1) policy iteration methods, which alternate between estimating the value function un- der the current policy and improving the policy (Bertsekas, 2005); (2) policy gradient methods, which use an estima- tor of the gradient of the expected return (total reward) ob- tained from sample trajectories (Peters & Schaal, 2008a) (and which, as we later discuss, have a close connection to policy iteration); and (3) derivative-free optimization meth- ods, such as the cross-entropy method (CEM) and covari- ance matrix adaptation (CMA), which treat the return as a black box function to be optimized in terms of the policy parameters (Szita & L¨orincz, 2006). | 1502.05477#2 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 2,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "# 1 Introduction\nMost algorithms for policy optimization can be classiï¬ed into three broad categories: (1) policy iteration methods, which alternate between estimating the value function un- der the current policy and improving the policy (Bertsekas, 2005); (2) policy gradient methods, which use an estima- tor of the gradient of the expected return (total reward) ob- tained from sample trajectories (Peters & Schaal, 2008a) (and which, as we later discuss, have a close connection to policy iteration); and (3) derivative-free optimization meth- ods, such as the cross-entropy method (CEM) and covari- ance matrix adaptation (CMA), which treat the return as a black box function to be optimized in terms of the policy parameters (Szita & L¨orincz, 2006).",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 2 | 1
# INTRODUCTION
There is a rich history of the use of synthetic tasks in machine learning, from the XOR problem which helped motivate neural networks (Minsky & Papert, 1969; Rumelhart et al., 1985), to circle and ring datasets that helped motivate some of the most well-known clustering and semi-supervised learning algorithms (Ng et al., 2002; Zhu et al., 2003), Mackey Glass equations for time series (M¨uller et al., 1997), and so on â in fact some of the well known UCI datasets (Bache & Lichman, 2013) are synthetic as well (e.g., waveform). Recent work continues this trend. For example, in the area of developing learning algorithms with a memory component synthetic datasets were used to help develop both the Neural Turing Machine of Graves et al. (2014) and the Memory Networks of Weston et al. (2014), the latter of which is relevant to this work. | 1502.05698#2 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 2,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "1\n# INTRODUCTION\nThere is a rich history of the use of synthetic tasks in machine learning, from the XOR problem which helped motivate neural networks (Minsky & Papert, 1969; Rumelhart et al., 1985), to circle and ring datasets that helped motivate some of the most well-known clustering and semi-supervised learning algorithms (Ng et al., 2002; Zhu et al., 2003), Mackey Glass equations for time series (M¨uller et al., 1997), and so on â in fact some of the well known UCI datasets (Bache & Lichman, 2013) are synthetic as well (e.g., waveform). Recent work continues this trend. For example, in the area of developing learning algorithms with a memory component synthetic datasets were used to help develop both the Neural Turing Machine of Graves et al. (2014) and the Memory Networks of Weston et al. (2014), the latter of which is relevant to this work.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 3 | In this article, we ï¬rst prove that minimizing a certain sur- rogate objective function guarantees policy improvement with non-trivial step sizes. Then we make a series of ap- proximations to the theoretically-justiï¬ed algorithm, yield- ing a practical algorithm, which we call trust region pol- icy optimization (TRPO). We describe two variants of this algorithm: ï¬rst, the single-path method, which can be ap- plied in the model-free setting; second, the vine method, which requires the system to be restored to particular states, which is typically only possible in simulation. These al- gorithms are scalable and can optimize nonlinear policies with tens of thousands of parameters, which have previ- ously posed a major challenge for model-free policy search (Deisenroth et al., 2013). In our experiments, we show that the same TRPO methods can learn complex policies for swimming, hopping, and walking, as well as playing Atari games directly from raw images.
General derivative-free stochastic optimization methods such as CEM and CMA are preferred on many prob- lems, because they achieve good results while being sim- ple to understand and implement. For example, while | 1502.05477#3 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 3,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "In this article, we ï¬rst prove that minimizing a certain sur- rogate objective function guarantees policy improvement with non-trivial step sizes. Then we make a series of ap- proximations to the theoretically-justiï¬ed algorithm, yield- ing a practical algorithm, which we call trust region pol- icy optimization (TRPO). We describe two variants of this algorithm: ï¬rst, the single-path method, which can be ap- plied in the model-free setting; second, the vine method, which requires the system to be restored to particular states, which is typically only possible in simulation. These al- gorithms are scalable and can optimize nonlinear policies with tens of thousands of parameters, which have previ- ously posed a major challenge for model-free policy search (Deisenroth et al., 2013). In our experiments, we show that the same TRPO methods can learn complex policies for swimming, hopping, and walking, as well as playing Atari games directly from raw images.\nGeneral derivative-free stochastic optimization methods such as CEM and CMA are preferred on many prob- lems, because they achieve good results while being sim- ple to understand and implement. For example, while",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 3 | One of the reasons for the interest in synthetic data is that it can be easier to develop new techniques using it. It is well known that working with large amounts of real data (âbig dataâ) tends to lead researchers to simpler models as âsimple models and a lot of data trump more elaborate models based on less dataâ (Halevy et al., 2009). For example, N -grams for language modeling work well relative to existing competing methods, but are far from being a model that truly understands text. As researchers we can become stuck in local minima in algorithm space; development of synthetic data is one way to try and break out of that.
In this work we propose a framework and a set of synthetic tasks for the goal of helping to develop learning algorithms for text understanding and reasoning. While it is relatively difï¬cult to auto- matically evaluate the performance of an agent in general dialogue â a long term-goal of AI â it is relatively easy to evaluate responses to input questions, i.e., the task of question answering (QA). Question answering is incredibly broad: more or less any task one can think of can be cast into this setup. This enables us to propose a wide ranging set of different tasks, that test different capabilities of learning algorithms, under a common framework. | 1502.05698#3 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 3,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "One of the reasons for the interest in synthetic data is that it can be easier to develop new techniques using it. It is well known that working with large amounts of real data (âbig dataâ) tends to lead researchers to simpler models as âsimple models and a lot of data trump more elaborate models based on less dataâ (Halevy et al., 2009). For example, N -grams for language modeling work well relative to existing competing methods, but are far from being a model that truly understands text. As researchers we can become stuck in local minima in algorithm space; development of synthetic data is one way to try and break out of that.\nIn this work we propose a framework and a set of synthetic tasks for the goal of helping to develop learning algorithms for text understanding and reasoning. While it is relatively difï¬cult to auto- matically evaluate the performance of an agent in general dialogue â a long term-goal of AI â it is relatively easy to evaluate responses to input questions, i.e., the task of question answering (QA). Question answering is incredibly broad: more or less any task one can think of can be cast into this setup. This enables us to propose a wide ranging set of different tasks, that test different capabilities of learning algorithms, under a common framework.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 4 | Proceedings of the 31 st International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37. Copy- right 2015 by the author(s).
# 2 Preliminaries
Consider an inï¬nite-horizon discounted Markov decision process (MDP), deï¬ned by the tuple (S, A, P, r, Ï0, γ), where S is a ï¬nite set of states, A is a ï¬nite set of actions, P : S à A à S â R is the transition probability distriTrust Region Policy Optimization
bution, r : S â R is the reward function, Ï0 : S â R is the distribution of the initial state s0, and γ â (0, 1) is the discount factor.
Let Ï denote a stochastic policy Ï : S à A â [0, 1], and let η(Ï) denote its expected discounted reward:
(17) = Eso ,ao,. [en r(se ] , where $0 ~ po(So), ae ~ T(at|S2), Se41 ~ P(St41| St, a4). | 1502.05477#4 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 4,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Proceedings of the 31 st International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37. Copy- right 2015 by the author(s).\n# 2 Preliminaries\nConsider an inï¬nite-horizon discounted Markov decision process (MDP), deï¬ned by the tuple (S, A, P, r, Ï0, γ), where S is a ï¬nite set of states, A is a ï¬nite set of actions, P : S à A à S â R is the transition probability distriTrust Region Policy Optimization\nbution, r : S â R is the reward function, Ï0 : S â R is the distribution of the initial state s0, and γ â (0, 1) is the discount factor.\nLet Ï denote a stochastic policy Ï : S à A â [0, 1], and let η(Ï) denote its expected discounted reward:\n(17) = Eso ,ao,. [en r(se ] , where $0 ~ po(So), ae ~ T(at|S2), Se41 ~ P(St41| St, a4).",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 4 | Our tasks are built with a uniï¬ed underlying simulation of a physical world, akin to a classic text adventure game (Montfort, 2005) whereby actors move around manipulating objects and interacting
1
# Under review as a conference paper at ICLR 2016
with each other. As the simulation runs, grounded text and question answer pairs are simultaneously generated. Our goal is to categorize different kinds of questions into skill sets, which become our tasks. Our hope is that the analysis of performance on these tasks will help expose weaknesses of current models and help motivate new algorithm designs that alleviate these weaknesses. We further envision this as a feedback loop where new tasks can then be designed in response, perhaps in an adversarial fashion, in order to break the new models.
The tasks we design are detailed in Section 3, and the simulation used to generate them in Section 4. In Section 5 we give benchmark results of standard methods on our tasks, and analyse their successes and failures. In order to exemplify the kind of feedback loop between algorithm development and task development we envision, in Section A we propose a set of improvements to the recent Memory Network method, which has shown to give promising performance in QA. We show our proposed approach does indeed give improved performance on some tasks, but is still unable to solve some of them, which we consider as open problems.
# 2 RELATED WORK | 1502.05698#4 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 4,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Our tasks are built with a uniï¬ed underlying simulation of a physical world, akin to a classic text adventure game (Montfort, 2005) whereby actors move around manipulating objects and interacting\n1\n# Under review as a conference paper at ICLR 2016\nwith each other. As the simulation runs, grounded text and question answer pairs are simultaneously generated. Our goal is to categorize different kinds of questions into skill sets, which become our tasks. Our hope is that the analysis of performance on these tasks will help expose weaknesses of current models and help motivate new algorithm designs that alleviate these weaknesses. We further envision this as a feedback loop where new tasks can then be designed in response, perhaps in an adversarial fashion, in order to break the new models.\nThe tasks we design are detailed in Section 3, and the simulation used to generate them in Section 4. In Section 5 we give benchmark results of standard methods on our tasks, and analyse their successes and failures. In order to exemplify the kind of feedback loop between algorithm development and task development we envision, in Section A we propose a set of improvements to the recent Memory Network method, which has shown to give promising performance in QA. We show our proposed approach does indeed give improved performance on some tasks, but is still unable to solve some of them, which we consider as open problems.\n# 2 RELATED WORK",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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] |
1502.05477 | 5 | it(s) = arg max, A,(s, a), improves the policy if there is at least one state-action pair with a positive advantage value and nonzero state visitation probability, otherwise the algo- rithm has converged to the optimal policy. However, in the approximate setting, it will typically be unavoidable, due to estimation and approximation error, that there will be some states s for which the expected advantage is negative, that is, >, #(a|s)A,(s,a) < 0. The complex dependency of p(s) on 7 makes Equation (2) difficult to optimize di- rectly. Instead, we introduce the following local approxi- mation to 17:
We will use the following standard deï¬nitions of the state- action value function QÏ, the value function VÏ, and the advantage function AÏ:
LÏ(ËÏ) = η(Ï) + s ÏÏ(s) a ËÏ(a|s)AÏ(s, a). (3) | 1502.05477#5 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 5,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "it(s) = arg max, A,(s, a), improves the policy if there is at least one state-action pair with a positive advantage value and nonzero state visitation probability, otherwise the algo- rithm has converged to the optimal policy. However, in the approximate setting, it will typically be unavoidable, due to estimation and approximation error, that there will be some states s for which the expected advantage is negative, that is, >, #(a|s)A,(s,a) < 0. The complex dependency of p(s) on 7 makes Equation (2) difficult to optimize di- rectly. Instead, we introduce the following local approxi- mation to 17:\nWe will use the following standard deï¬nitions of the state- action value function QÏ, the value function VÏ, and the advantage function AÏ:\nLÏ(ËÏ) = η(Ï) + s ÏÏ(s) a ËÏ(a|s)AÏ(s, a). (3)",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 5 | # 2 RELATED WORK
Several projects targeting language understanding using QA-based strategies have recently emerged. Unlike tasks like dialogue or summarization, QA is easy to evaluate (especially in true/false or multiple choice scenarios) and hence makes it an appealing research avenue. The difï¬culty lies in the deï¬nition of questions: they must be unambiguously answerable by adult humans (or children), but still require some thinking. The Allen Institute for AIâs ï¬agship project ARISTO1 is organized around a collection of QA tasks derived from increasingly difï¬cult science exams, at the 4th, 8th, and 12th grade levels. Richardson et al. (2013) proposed the MCTest2 a set of 660 stories and associated questions intended for research on the machine comprehension of text. Each question requires the reader to understand different aspects of the story. | 1502.05698#5 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 5,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "# 2 RELATED WORK\nSeveral projects targeting language understanding using QA-based strategies have recently emerged. Unlike tasks like dialogue or summarization, QA is easy to evaluate (especially in true/false or multiple choice scenarios) and hence makes it an appealing research avenue. The difï¬culty lies in the deï¬nition of questions: they must be unambiguously answerable by adult humans (or children), but still require some thinking. The Allen Institute for AIâs ï¬agship project ARISTO1 is organized around a collection of QA tasks derived from increasingly difï¬cult science exams, at the 4th, 8th, and 12th grade levels. Richardson et al. (2013) proposed the MCTest2 a set of 660 stories and associated questions intended for research on the machine comprehension of text. Each question requires the reader to understand different aspects of the story.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 6 | LÏ(ËÏ) = η(Ï) + s ÏÏ(s) a ËÏ(a|s)AÏ(s, a). (3)
Qe (St, 4t) = Eo ys jarg1 5... » ârod ; 1=0 Vx(St) = ay sega, » ârea ; 1=0 A,(s,a) = Q,(s,a) â Vr(s), where ay ~ 7(at|Se), Se41 ~ P(Sr41|St, ae) for t > 0.
Note that LÏ uses the visitation frequency ÏÏ rather than ÏËÏ, ignoring changes in state visitation density due to changes in the policy. However, if we have a parameter- ized policy Ïθ, where Ïθ(a|s) is a differentiable function of the parameter vector θ, then LÏ matches η to ï¬rst order (see Kakade & Langford (2002)). That is, for any parame- ter value θ0,
The following useful identity expresses the expected return of another policy ËÏ in terms of the advantage over Ï, accu- mulated over timesteps (see Kakade & Langford (2002) or Appendix A for proof): | 1502.05477#6 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 6,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "LÏ(ËÏ) = η(Ï) + s ÏÏ(s) a ËÏ(a|s)AÏ(s, a). (3)\nQe (St, 4t) = Eo ys jarg1 5... » ârod ; 1=0 Vx(St) = ay sega, » ârea ; 1=0 A,(s,a) = Q,(s,a) â Vr(s), where ay ~ 7(at|Se), Se41 ~ P(Sr41|St, ae) for t > 0.\nNote that LÏ uses the visitation frequency ÏÏ rather than ÏËÏ, ignoring changes in state visitation density due to changes in the policy. However, if we have a parameter- ized policy Ïθ, where Ïθ(a|s) is a differentiable function of the parameter vector θ, then LÏ matches η to ï¬rst order (see Kakade & Langford (2002)). That is, for any parame- ter value θ0,\nThe following useful identity expresses the expected return of another policy ËÏ in terms of the advantage over Ï, accu- mulated over timesteps (see Kakade & Langford (2002) or Appendix A for proof):",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 6 | These two initiatives go in a promising direction but interpreting the results on these benchmarks remain complicated. Indeed, no system has yet been able to fully solve the proposed tasks and since many sub-tasks need to be solved to answer any of their questions (coreference, deduction, use of common-sense, etc.), it is difï¬cult to clearly identify capabilities and limitations of these systems and hence to propose improvements and modiï¬cations. As a result, conclusions drawn from these projects are not much clearer than that coming from more traditional works on QA over large-scale Knowledge Bases (Berant et al., 2013; Fader et al., 2014). Besides, the best performing systems are based on hand-crafted patterns and features, and/or statistics acquired on very large corpora. It is difï¬cult to argue that such systems actually understand language and are not simply light upgrades of traditional information extraction methods (Yao et al., 2014). The system of Berant et al. (2014) is more evolved since it builds a structured representation of a text and of a question to answer. Despite its potential this method remains highly domain speciï¬c and relies on a lot of prior knowledge. | 1502.05698#6 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 6,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "These two initiatives go in a promising direction but interpreting the results on these benchmarks remain complicated. Indeed, no system has yet been able to fully solve the proposed tasks and since many sub-tasks need to be solved to answer any of their questions (coreference, deduction, use of common-sense, etc.), it is difï¬cult to clearly identify capabilities and limitations of these systems and hence to propose improvements and modiï¬cations. As a result, conclusions drawn from these projects are not much clearer than that coming from more traditional works on QA over large-scale Knowledge Bases (Berant et al., 2013; Fader et al., 2014). Besides, the best performing systems are based on hand-crafted patterns and features, and/or statistics acquired on very large corpora. It is difï¬cult to argue that such systems actually understand language and are not simply light upgrades of traditional information extraction methods (Yao et al., 2014). The system of Berant et al. (2014) is more evolved since it builds a structured representation of a text and of a question to answer. Despite its potential this method remains highly domain speciï¬c and relies on a lot of prior knowledge.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 7 | nm) = n(%) + Eso,a0,- [eo A, (st, at ] (1)
where the notation Es0,a0,···â¼ËÏ [. . . ] indicates that actions are sampled at â¼ ËÏ(·|st). Let ÏÏ be the (unnormalized) discounted visitation frequencies
ÏÏ(s) = P (s0 = s)+γP (s1 = s)+γ2P (s2 = s)+. . . ,
Liz, (To) = (75) VoL, (7)|g_9, = Von(t)|o-9,- (4)
Equation (4) implies that a sufï¬ciently small step Ïθ0 â ËÏ that improves LÏθold will also improve η, but does not give us any guidance on how big of a step to take.
To address this issue,|Kakade & Langford|(2002) proposed a policy updating scheme called conservative policy iter- ation, for which they could provide explicit lower bounds on the improvement of 7. To define the conservative pol- icy iteration update, let 7.14 denote the current policy, and let 7â = argmax,, L;.,,(7â). The new policy tnew was defined to be the following mixture: | 1502.05477#7 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 7,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "nm) = n(%) + Eso,a0,- [eo A, (st, at ] (1)\nwhere the notation Es0,a0,···â¼ËÏ [. . . ] indicates that actions are sampled at â¼ ËÏ(·|st). Let ÏÏ be the (unnormalized) discounted visitation frequencies\nÏÏ(s) = P (s0 = s)+γP (s1 = s)+γ2P (s2 = s)+. . . ,\nLiz, (To) = (75) VoL, (7)|g_9, = Von(t)|o-9,- (4)\nEquation (4) implies that a sufï¬ciently small step Ïθ0 â ËÏ that improves LÏθold will also improve η, but does not give us any guidance on how big of a step to take.\nTo address this issue,|Kakade & Langford|(2002) proposed a policy updating scheme called conservative policy iter- ation, for which they could provide explicit lower bounds on the improvement of 7. To define the conservative pol- icy iteration update, let 7.14 denote the current policy, and let 7â = argmax,, L;.,,(7â). The new policy tnew was defined to be the following mixture:",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 7 | Based on these observations, we chose to conceive a collection of much simpler QA tasks, with the main objective that failure or success of a system on any of them can unequivocally provide feedback on its capabilities. In that, we are close to the Winograd Schema Challenge Levesque et al. (2011), which is organized around simple statements followed by a single binary choice question such as: âJoan made sure to thank Susan for all the help she had received. Who had received the help? Joan or Susan?â. In this challenge, and our tasks, it is straightforward to interpret results. Yet, where the Winograd Challenge is mostly centered around evaluating if systems can acquire and make use of background knowledge that is not expressed in the words of the statement, our tasks are self-contained and are more diverse. By self-contained we mean our tasks come with both training data and evaluation data, rather than just the latter as in the case of ARISTO and the Winograd Challenge. MCTest has a train/test split but the training set is likely too small to capture all the reasoning needed to do well on the test set. In our setup one can assess the amount of training examples needed to perform well (which can be increased as | 1502.05698#7 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 7,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Based on these observations, we chose to conceive a collection of much simpler QA tasks, with the main objective that failure or success of a system on any of them can unequivocally provide feedback on its capabilities. In that, we are close to the Winograd Schema Challenge Levesque et al. (2011), which is organized around simple statements followed by a single binary choice question such as: âJoan made sure to thank Susan for all the help she had received. Who had received the help? Joan or Susan?â. In this challenge, and our tasks, it is straightforward to interpret results. Yet, where the Winograd Challenge is mostly centered around evaluating if systems can acquire and make use of background knowledge that is not expressed in the words of the statement, our tasks are self-contained and are more diverse. By self-contained we mean our tasks come with both training data and evaluation data, rather than just the latter as in the case of ARISTO and the Winograd Challenge. MCTest has a train/test split but the training set is likely too small to capture all the reasoning needed to do well on the test set. In our setup one can assess the amount of training examples needed to perform well (which can be increased as",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 8 | where s0 â¼ Ï0 and the actions are chosen according to Ï. We can rewrite Equation (1) with a sum over states instead of timesteps:
Tnew(als) = (1 â a)to1a(a|s) + a7â (als). (5)
Kakade and Langford derived the following lower bound:
n(i +0 Plo = ale Do Aals) 7â Ax (s, a) oo) +E oP = sl) 30 ( + Dl DH als)Ax(s, 4). (2)
This equation implies that any policy update 7 â 7 that has a nonnegative expected advantage at every state s, ie, 0, 7(a|s)Ax(s,a) > 0, is guaranteed to increase the policy performance 7), or leave it constant in the case that the expected advantage is zero everywhere. This im- plies the classic result that the update performed by ex- act policy iteration, which uses the deterministic policy
2â¬7 (Tew) 2 Lara (Tuew) â aâ where ⬠= max|Eq.nâ(a\s) [Ax(s,@)]|. (6) | 1502.05477#8 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 8,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "where s0 â¼ Ï0 and the actions are chosen according to Ï. We can rewrite Equation (1) with a sum over states instead of timesteps:\nTnew(als) = (1 â a)to1a(a|s) + a7â (als). (5)\nKakade and Langford derived the following lower bound:\nn(i +0 Plo = ale Do Aals) 7â Ax (s, a) oo) +E oP = sl) 30 ( + Dl DH als)Ax(s, 4). (2)\nThis equation implies that any policy update 7 â 7 that has a nonnegative expected advantage at every state s, ie, 0, 7(a|s)Ax(s,a) > 0, is guaranteed to increase the policy performance 7), or leave it constant in the case that the expected advantage is zero everywhere. This im- plies the classic result that the update performed by ex- act policy iteration, which uses the deterministic policy\n2â¬7 (Tew) 2 Lara (Tuew) â aâ where ⬠= max|Eq.nâ(a\\s) [Ax(s,@)]|. (6)",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 8 | capture all the reasoning needed to do well on the test set. In our setup one can assess the amount of training examples needed to perform well (which can be increased as desired) and commonsense knowledge and reasoning required for the test set should be contained in the training set. In terms of diversity, some of our tasks are related to existing setups but we also propose many additional ones; tasks 8 and 9 are inspired by previous work on lambda dependency-based compositional semantics (Liang et al., 2013; Liang, 2013) for instance. For us, each task checks one skill that the system must | 1502.05698#8 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 8,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "capture all the reasoning needed to do well on the test set. In our setup one can assess the amount of training examples needed to perform well (which can be increased as desired) and commonsense knowledge and reasoning required for the test set should be contained in the training set. In terms of diversity, some of our tasks are related to existing setups but we also propose many additional ones; tasks 8 and 9 are inspired by previous work on lambda dependency-based compositional semantics (Liang et al., 2013; Liang, 2013) for instance. For us, each task checks one skill that the system must",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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] |
1502.05477 | 9 | (We have modiï¬ed it to make it slightly weaker but sim- pler.) Note, however, that so far this bound only applies to mixture policies generated by Equation (5). This policy class is unwieldy and restrictive in practice, and it is desir- able for a practical policy update scheme to be applicable to all general stochastic policy classes.
# 3 Monotonic Improvement Guarantee for General Stochastic Policies
Equation (6), which applies to conservative policy iteration, implies that a policy update that improves the right-hand
Trust Region Policy Optimization
side is guaranteed to improve the true performance 7. Our principal theoretical result is that the policy improvement bound in Equation (6) can be extended to general stochas- tic policies, rather than just mixture polices, by replacing a with a distance measure between 7 and 7, and changing the constant ⬠appropriately. Since mixture policies are rarely used in practice, this result is crucial for extending the im- provement guarantee to practical problems. The particular distance measure we use is the total variation divergence, which is defined by Dry (p || ¢) = 4°, \pi â ai| for dis- crete probability distributions p, qf! Define DIX (a, 7) as | 1502.05477#9 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 9,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "(We have modiï¬ed it to make it slightly weaker but sim- pler.) Note, however, that so far this bound only applies to mixture policies generated by Equation (5). This policy class is unwieldy and restrictive in practice, and it is desir- able for a practical policy update scheme to be applicable to all general stochastic policy classes.\n# 3 Monotonic Improvement Guarantee for General Stochastic Policies\nEquation (6), which applies to conservative policy iteration, implies that a policy update that improves the right-hand\nTrust Region Policy Optimization\nside is guaranteed to improve the true performance 7. Our principal theoretical result is that the policy improvement bound in Equation (6) can be extended to general stochas- tic policies, rather than just mixture polices, by replacing a with a distance measure between 7 and 7, and changing the constant ⬠appropriately. Since mixture policies are rarely used in practice, this result is crucial for extending the im- provement guarantee to practical problems. The particular distance measure we use is the total variation divergence, which is defined by Dry (p || ¢) = 4°, \\pi â ai| for dis- crete probability distributions p, qf! Define DIX (a, 7) as",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 9 | 1
http://allenai.org/aristo.html
2
http://research.microsoft.com/mct
2
# Under review as a conference paper at ICLR 2016
have and we postulate that performing well on all of them is a prerequisite for any system aiming at full text understanding and reasoning.
# 3 THE TASKS
Principles Our main idea is to provide a set of tasks, in a similar way to how software testing is built in computer science. Ideally each task is a âleafâ test case, as independent from oth- ers as possible, and tests in the simplest way possible one aspect of intended behavior. Subse- quent (ânon-leafâ) tests can build on these by testing combinations as well. The tasks are pub- licly available at http://fb.ai/babi. Source code to generate the tasks is available at https://github.com/facebook/bAbI-tasks. | 1502.05698#9 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 9,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "1\nhttp://allenai.org/aristo.html\n2\nhttp://research.microsoft.com/mct\n2\n# Under review as a conference paper at ICLR 2016\nhave and we postulate that performing well on all of them is a prerequisite for any system aiming at full text understanding and reasoning.\n# 3 THE TASKS\nPrinciples Our main idea is to provide a set of tasks, in a similar way to how software testing is built in computer science. Ideally each task is a âleafâ test case, as independent from oth- ers as possible, and tests in the simplest way possible one aspect of intended behavior. Subse- quent (ânon-leafâ) tests can build on these by testing combinations as well. The tasks are pub- licly available at http://fb.ai/babi. Source code to generate the tasks is available at https://github.com/facebook/bAbI-tasks.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 10 | DIRS(m.#) = max Drv (w(-|s) || #|s))Algorithm 1 Policy iteration algorithm guaranteeing non- decreasing expected return η
Initialize Ï0. for i = 0, 1, 2, . . . until convergence do Compute all advantage values AÏi(s, a). Solve the constrained optimization problem
Tiga = arg max [Lx,(7) â CDKT* (7, 7)| where C' = 4ey/(1 â 7)? and L,(n)=n(m) +> pals) om (as) An, (8+a)
# end for
Theorem 1. Let α = Dmax ing bound holds: TV (Ïold, Ïnew). Then the followsey (1-7? where ⬠= max |A,(s, a)| sa (Tew) = Larosa (Mew) â
(8)
is a type of minorization-maximization (MM) algorithm (Hunter & Lange, 2004), which is a class of methods that also includes expectation maximization. In the terminol- ogy of MM algorithms, Mi is the surrogate function that minorizes η with equality at Ïi. This algorithm is also rem- iniscent of proximal gradient methods and mirror descent. | 1502.05477#10 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 10,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "DIRS(m.#) = max Drv (w(-|s) || #|s))Algorithm 1 Policy iteration algorithm guaranteeing non- decreasing expected return η\nInitialize Ï0. for i = 0, 1, 2, . . . until convergence do Compute all advantage values AÏi(s, a). Solve the constrained optimization problem\nTiga = arg max [Lx,(7) â CDKT* (7, 7)| where C' = 4ey/(1 â 7)? and L,(n)=n(m) +> pals) om (as) An, (8+a)\n# end for\nTheorem 1. Let α = Dmax ing bound holds: TV (Ïold, Ïnew). Then the followsey (1-7? where ⬠= max |A,(s, a)| sa (Tew) = Larosa (Mew) â\n(8)\nis a type of minorization-maximization (MM) algorithm (Hunter & Lange, 2004), which is a class of methods that also includes expectation maximization. In the terminol- ogy of MM algorithms, Mi is the surrogate function that minorizes η with equality at Ïi. This algorithm is also rem- iniscent of proximal gradient methods and mirror descent.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 10 | Each task provides a set of training and test data, with the intention that a successful model performs well on test data. Following Weston et al. (2014), the supervision in the training set is given by the true answers to questions, and the set of relevant statements for answering a given question, which may or may not be used by the learner. We set up the tasks so that correct answers are limited to a single word (Q: Where is Mark? A: bathroom), or else a list of words (Q: What is Mark holding?) as evaluation is then clear-cut, and is measured simply as right or wrong.
All of the tasks are noiseless and a human able to read that language can potentially achieve 100% accuracy. We tried to choose tasks that are natural to a human: they are based on simple usual situ- ations and no background in areas such as formal semantics, machine learning, logic or knowledge representation is required for an adult to solve them.
The data itself is produced using a simple simulation of characters and objects moving around and interacting in locations, described in Section 4. The simulation allows us to generate data in many different scenarios where the true labels are known by grounding to the simulation. For each task, we describe it by giving a small sample of the dataset including statements, questions and the true labels (in red) in Tables 1 and 2. | 1502.05698#10 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 10,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Each task provides a set of training and test data, with the intention that a successful model performs well on test data. Following Weston et al. (2014), the supervision in the training set is given by the true answers to questions, and the set of relevant statements for answering a given question, which may or may not be used by the learner. We set up the tasks so that correct answers are limited to a single word (Q: Where is Mark? A: bathroom), or else a list of words (Q: What is Mark holding?) as evaluation is then clear-cut, and is measured simply as right or wrong.\nAll of the tasks are noiseless and a human able to read that language can potentially achieve 100% accuracy. We tried to choose tasks that are natural to a human: they are based on simple usual situ- ations and no background in areas such as formal semantics, machine learning, logic or knowledge representation is required for an adult to solve them.\nThe data itself is produced using a simple simulation of characters and objects moving around and interacting in locations, described in Section 4. The simulation allows us to generate data in many different scenarios where the true labels are known by grounding to the simulation. For each task, we describe it by giving a small sample of the dataset including statements, questions and the true labels (in red) in Tables 1 and 2.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 11 | We provide two proofs in the appendix. The ï¬rst proof ex- tends Kakade and Langfordâs result using the fact that the random variables from two distributions with total varia- tion divergence less than α can be coupled, so that they are equal with probability 1 â α. The second proof uses per- turbation theory.
Trust region policy optimization, which we propose in the following section, is an approximation to Algorithm 1, which uses a constraint on the KL divergence rather than a penalty to robustly allow large updates.
# 4 Optimization of Parameterized Policies
Next, we note the following relationship between the to- tal variation divergence and the KL divergence (Pollard (2000), Ch. 3): Drv(p || 4g)? < Dxx(p || q). Let Die*(a,7) = maxs Dxi(z(-|s) || 7(-|s)). The follow- ing bound then follows directly from Theorem|I}
In the previous section, we considered the policy optimiza- tion problem independently of the parameterization of Ï and under the assumption that the policy can be evaluated at all states. We now describe how to derive a practical algorithm from these theoretical foundations, under ï¬nite sample counts and arbitrary parameterizations.
(a) 2 Lx (a) â CDR (, 7), dey (l-7)?" where C' = (9) | 1502.05477#11 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 11,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "We provide two proofs in the appendix. The ï¬rst proof ex- tends Kakade and Langfordâs result using the fact that the random variables from two distributions with total varia- tion divergence less than α can be coupled, so that they are equal with probability 1 â α. The second proof uses per- turbation theory.\nTrust region policy optimization, which we propose in the following section, is an approximation to Algorithm 1, which uses a constraint on the KL divergence rather than a penalty to robustly allow large updates.\n# 4 Optimization of Parameterized Policies\nNext, we note the following relationship between the to- tal variation divergence and the KL divergence (Pollard (2000), Ch. 3): Drv(p || 4g)? < Dxx(p || q). Let Die*(a,7) = maxs Dxi(z(-|s) || 7(-|s)). The follow- ing bound then follows directly from Theorem|I}\nIn the previous section, we considered the policy optimiza- tion problem independently of the parameterization of Ï and under the assumption that the policy can be evaluated at all states. We now describe how to derive a practical algorithm from these theoretical foundations, under ï¬nite sample counts and arbitrary parameterizations.\n(a) 2 Lx (a) â CDR (, 7), dey (l-7)?\" where C' = (9)",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 11 | Single Supporting Fact Task 1 consists of questions where a previously given single supporting fact, potentially amongst a set of other irrelevant facts, provides the answer. We ï¬rst test one of the simplest cases of this, by asking for the location of a person, e.g. âMary travelled to the ofï¬ce. Where is Mary?â. This kind of task was already employed in Weston et al. (2014). It can be considered the simplest case of some real world QA datasets such as in Fader et al. (2013).
Two or Three Supporting Facts A harder task is to answer questions where two supporting state- ments have to be chained to answer the question, as in task 2, where to answer the question âWhere is the football?â one has to combine information from two sentences âJohn is in the playgroundâ and âJohn picked up the footballâ. Again, this kind of task was already used in Weston et al. (2014). Similarly, one can make a task with three supporting facts, given in task 3, whereby the ï¬rst three statements are all required to answer the question âWhere was the apple before the kitchen?â. | 1502.05698#11 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 11,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Single Supporting Fact Task 1 consists of questions where a previously given single supporting fact, potentially amongst a set of other irrelevant facts, provides the answer. We ï¬rst test one of the simplest cases of this, by asking for the location of a person, e.g. âMary travelled to the ofï¬ce. Where is Mary?â. This kind of task was already employed in Weston et al. (2014). It can be considered the simplest case of some real world QA datasets such as in Fader et al. (2013).\nTwo or Three Supporting Facts A harder task is to answer questions where two supporting state- ments have to be chained to answer the question, as in task 2, where to answer the question âWhere is the football?â one has to combine information from two sentences âJohn is in the playgroundâ and âJohn picked up the footballâ. Again, this kind of task was already used in Weston et al. (2014). Similarly, one can make a task with three supporting facts, given in task 3, whereby the ï¬rst three statements are all required to answer the question âWhere was the apple before the kitchen?â.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 12 | (a) 2 Lx (a) â CDR (, 7), dey (l-7)?" where C' = (9)
Algorithm 1 describes an approximate policy iteration scheme based on the policy improvement bound in Equa- tion (9). Note that for now, we assume exact evaluation of the advantage values AÏ.
It follows from Equation (9) that Algorithm 1 is guaranteed to generate a monotonically improving sequence of policies η(Ï0) ⤠η(Ï1) ⤠η(Ï2) ⤠. . . . To see this, let Mi(Ï) = LÏi(Ï) â CDmax
Since we consider parameterized policies 7(a|s) with pa- rameter vector 0, we will overload our previous notation to use functions of @ rather than 7, e.g. 7(8) := n(7), L6(0) := Lo (7g), and Dxx(4 || 8) := Dx(70 || 7). We will use Ao1q to denote the previous policy parameters that we want to improve upon.
The preceding section showed that η(θ) ⥠Lθold (θ) â CDmax KL (θold, θ), with equality at θ = θold. Thus, by per- forming the following maximization, we are guaranteed to improve the true objective η: | 1502.05477#12 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 12,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "(a) 2 Lx (a) â CDR (, 7), dey (l-7)?\" where C' = (9)\nAlgorithm 1 describes an approximate policy iteration scheme based on the policy improvement bound in Equa- tion (9). Note that for now, we assume exact evaluation of the advantage values AÏ.\nIt follows from Equation (9) that Algorithm 1 is guaranteed to generate a monotonically improving sequence of policies η(Ï0) ⤠η(Ï1) ⤠η(Ï2) ⤠. . . . To see this, let Mi(Ï) = LÏi(Ï) â CDmax\nSince we consider parameterized policies 7(a|s) with pa- rameter vector 0, we will overload our previous notation to use functions of @ rather than 7, e.g. 7(8) := n(7), L6(0) := Lo (7g), and Dxx(4 || 8) := Dx(70 || 7). We will use Ao1q to denote the previous policy parameters that we want to improve upon.\nThe preceding section showed that η(θ) ⥠Lθold (θ) â CDmax KL (θold, θ), with equality at θ = θold. Thus, by per- forming the following maximization, we are guaranteed to improve the true objective η:",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 12 | Two or Three Argument Relations To answer questions the ability to differentiate and recognize subjects and objects is crucial. In task 4 we consider the extreme case where sentences feature re- ordered words, i.e. a bag-of-words will not work. For example, the questions âWhat is north of the bedroom?â and âWhat is the bedroom north of?â have exactly the same words, but a different order, with different answers. A step further, sometimes one needs to differentiate three separate arguments. Task 5 involves statements like âJeff was given the milk by Billâ and then queries who is the giver, receiver or which object is involved.
Yes/No Questions Task 6 tests, on some of the simplest questions possible (speciï¬cally, ones with a single supporting fact), the ability of a model to answer true/false type questions like âIs John in the playground?â.
Counting and Lists/Sets Task 7 tests the ability of the QA system to perform simple counting operations, by asking about the number of objects with a certain property, e.g. âHow many objects is Daniel holding?â. Similarly, task 8 tests the ability to produce a set of single word answers in the form of a list, e.g. âWhat is Daniel holding?â. These tasks can be seen as QA tasks related to basic database search operations.
3 | 1502.05698#12 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 12,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Two or Three Argument Relations To answer questions the ability to differentiate and recognize subjects and objects is crucial. In task 4 we consider the extreme case where sentences feature re- ordered words, i.e. a bag-of-words will not work. For example, the questions âWhat is north of the bedroom?â and âWhat is the bedroom north of?â have exactly the same words, but a different order, with different answers. A step further, sometimes one needs to differentiate three separate arguments. Task 5 involves statements like âJeff was given the milk by Billâ and then queries who is the giver, receiver or which object is involved.\nYes/No Questions Task 6 tests, on some of the simplest questions possible (speciï¬cally, ones with a single supporting fact), the ability of a model to answer true/false type questions like âIs John in the playground?â.\nCounting and Lists/Sets Task 7 tests the ability of the QA system to perform simple counting operations, by asking about the number of objects with a certain property, e.g. âHow many objects is Daniel holding?â. Similarly, task 8 tests the ability to produce a set of single word answers in the form of a list, e.g. âWhat is Daniel holding?â. These tasks can be seen as QA tasks related to basic database search operations.\n3",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 13 | maximize θ [Lθold (θ) â CDmax KL (θold, θ)] .
η(Ïi+1) ⥠Mi(Ïi+1) by Equation (9) η(Ïi) = Mi(Ïi), therefore, η(Ïi+1) â η(Ïi) ⥠Mi(Ïi+1) â M (Ïi). (10)
Thus, by maximizing Mi at each iteration, we guarantee that the true objective η is non-decreasing. This algorithm
1Our result is straightforward to extend to continuous states and actions by replacing the sums with integrals.
In practice, if we used the penalty coefï¬cient C recom- mended by the theory above, the step sizes would be very small. One way to take larger steps in a robust way is to use a constraint on the KL divergence between the new policy and the old policy, i.e., a trust region constraint:
maximize θ Lθold (θ) (11)
subject to Dmax KL (θold, θ) ⤠δ.
Trust Region Policy Optimization | 1502.05477#13 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 13,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "maximize θ [Lθold (θ) â CDmax KL (θold, θ)] .\nη(Ïi+1) ⥠Mi(Ïi+1) by Equation (9) η(Ïi) = Mi(Ïi), therefore, η(Ïi+1) â η(Ïi) ⥠Mi(Ïi+1) â M (Ïi). (10)\nThus, by maximizing Mi at each iteration, we guarantee that the true objective η is non-decreasing. This algorithm\n1Our result is straightforward to extend to continuous states and actions by replacing the sums with integrals.\nIn practice, if we used the penalty coefï¬cient C recom- mended by the theory above, the step sizes would be very small. One way to take larger steps in a robust way is to use a constraint on the KL divergence between the new policy and the old policy, i.e., a trust region constraint:\nmaximize θ Lθold (θ) (11)\nsubject to Dmax KL (θold, θ) ⤠δ.\nTrust Region Policy Optimization",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05477 | 14 | maximize θ Lθold (θ) (11)
subject to Dmax KL (θold, θ) ⤠δ.
Trust Region Policy Optimization
This problem imposes a constraint that the KL divergence is bounded at every point in the state space. While it is motivated by the theory, this problem is impractical to solve due to the large number of constraints. Instead, we can use a heuristic approximation which considers the average KL divergence: Dict, (01,02) = Eswp [Dic(tt4, (-|8) |] 743(-[s))] We therefore propose solving the following optimization problem to generate a policy update:
Lθold(θ) Ïθold KL (θold, θ) ⤠δ.
maximize θ (12)
subject to D
Similar policy updates have been proposed in prior work (Bagnell & Schneider, 2003; Peters & Schaal, 2008b; Pe- ters et al., 2010), and we compare our approach to prior methods in Section 7 and in the experiments in Section 8. Our experiments also show that this type of constrained update has similar empirical performance to the maximum KL divergence constraint in Equation (11).
trajectories sampling two rollouts Sn An using CRN all state-action | pairs used in objective Po rollout set
sampling two rollouts using CRN | Po rollout set | 1502.05477#14 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 14,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "maximize θ Lθold (θ) (11)\nsubject to Dmax KL (θold, θ) ⤠δ.\nTrust Region Policy Optimization\nThis problem imposes a constraint that the KL divergence is bounded at every point in the state space. While it is motivated by the theory, this problem is impractical to solve due to the large number of constraints. Instead, we can use a heuristic approximation which considers the average KL divergence: Dict, (01,02) = Eswp [Dic(tt4, (-|8) |] 743(-[s))] We therefore propose solving the following optimization problem to generate a policy update:\nLθold(θ) Ïθold KL (θold, θ) ⤠δ.\nmaximize θ (12)\nsubject to D\nSimilar policy updates have been proposed in prior work (Bagnell & Schneider, 2003; Peters & Schaal, 2008b; Pe- ters et al., 2010), and we compare our approach to prior methods in Section 7 and in the experiments in Section 8. Our experiments also show that this type of constrained update has similar empirical performance to the maximum KL divergence constraint in Equation (11).\ntrajectories sampling two rollouts Sn An using CRN all state-action | pairs used in objective Po rollout set\nsampling two rollouts using CRN | Po rollout set",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05477 | 15 | trajectories sampling two rollouts Sn An using CRN all state-action | pairs used in objective Po rollout set
sampling two rollouts using CRN | Po rollout set
Figure 1. Left: illustration of single path procedure. Here, we generate a set of trajectories via simulation of the policy and in- corporate all state-action pairs (sn, an) into the objective. Right: illustration of vine procedure. We generate a set of âtrunkâ tra- jectories, and then generate âbranchâ rollouts from a subset of the reached states. For each of these states sn, we perform multiple actions (a1 and a2 here) and perform a rollout after each action, using common random numbers (CRN) to reduce the variance.
All that remains is to replace the expectations by sample averages and replace the Q value by an empirical estimate. The following sections describe two different schemes for performing this estimation.
# 5 Sample-Based Estimation of the Objective and Constraint
The previous section proposed a constrained optimization problem on the policy parameters (Equation (12)), which optimizes an estimate of the expected total reward η sub- ject to a constraint on the change in the policy at each up- date. This section describes how the objective and con- straint functions can be approximated using Monte Carlo simulation. | 1502.05477#15 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 15,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "trajectories sampling two rollouts Sn An using CRN all state-action | pairs used in objective Po rollout set\nsampling two rollouts using CRN | Po rollout set\nFigure 1. Left: illustration of single path procedure. Here, we generate a set of trajectories via simulation of the policy and in- corporate all state-action pairs (sn, an) into the objective. Right: illustration of vine procedure. We generate a set of âtrunkâ tra- jectories, and then generate âbranchâ rollouts from a subset of the reached states. For each of these states sn, we perform multiple actions (a1 and a2 here) and perform a rollout after each action, using common random numbers (CRN) to reduce the variance.\nAll that remains is to replace the expectations by sample averages and replace the Q value by an empirical estimate. The following sections describe two different schemes for performing this estimation.\n# 5 Sample-Based Estimation of the Objective and Constraint\nThe previous section proposed a constrained optimization problem on the policy parameters (Equation (12)), which optimizes an estimate of the expected total reward η sub- ject to a constraint on the change in the policy at each up- date. This section describes how the objective and con- straint functions can be approximated using Monte Carlo simulation.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05477 | 16 | The ï¬rst sampling scheme, which we call single path, is the one that is typically used for policy gradient estima- tion (Bartlett & Baxter, 2011), and is based on sampling individual trajectories. The second scheme, which we call vine, involves constructing a rollout set and then perform- ing multiple actions from each state in the rollout set. This method has mostly been explored in the context of policy it- eration methods (Lagoudakis & Parr, 2003; Gabillon et al., 2013).
We seek to solve the following optimization problem, ob- tained by expanding Lθold in Equation (12):
maximize θ s Ïθold (s) a Ïθ(a|s)Aθold (s, a) subject to D Ïθold KL (θold, θ) ⤠δ. (13)
We first replace )>, 9,,,(s) [-. .] in the objective by the ex- pectation TEE oxo sig [...]. Next, we replace the advan- tage values Ag,,, by the Q-values Qo,,, in Equation (13), which only changes the objective by a constant. Last, we replace the sum over the actions by an importance sampling estimator. Using q to denote the sampling distribution, the contribution of a single s,, to the loss function is | 1502.05477#16 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 16,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "The ï¬rst sampling scheme, which we call single path, is the one that is typically used for policy gradient estima- tion (Bartlett & Baxter, 2011), and is based on sampling individual trajectories. The second scheme, which we call vine, involves constructing a rollout set and then perform- ing multiple actions from each state in the rollout set. This method has mostly been explored in the context of policy it- eration methods (Lagoudakis & Parr, 2003; Gabillon et al., 2013).\nWe seek to solve the following optimization problem, ob- tained by expanding Lθold in Equation (12):\nmaximize θ s Ïθold (s) a Ïθ(a|s)Aθold (s, a) subject to D Ïθold KL (θold, θ) ⤠δ. (13)\nWe first replace )>, 9,,,(s) [-. .] in the objective by the ex- pectation TEE oxo sig [...]. Next, we replace the advan- tage values Ag,,, by the Q-values Qo,,, in Equation (13), which only changes the objective by a constant. Last, we replace the sum over the actions by an importance sampling estimator. Using q to denote the sampling distribution, the contribution of a single s,, to the loss function is",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 16 | Task 3: Three Supporting Facts John picked up the apple. John went to the ofï¬ce. John went to the kitchen. John dropped the apple. Where was the apple before the kitchen? A:ofï¬ce Task 4: Two Argument Relations The ofï¬ce is north of the bedroom. The bedroom is north of the bathroom. The kitchen is west of the garden. What is north of the bedroom? A: ofï¬ce What is the bedroom north of? A: bathroom Task 5: Three Argument Relations Mary gave the cake to Fred. Fred gave the cake to Bill. Jeff was given the milk by Bill. Who gave the cake to Fred? A: Mary Who did Fred give the cake to? A: Bill Task 6: Yes/No Questions John moved to the playground. Daniel went to the bathroom. John went back to the hallway. Is John in the playground? A:no Is Daniel in the bathroom? A:yes Task 7: Counting Daniel picked up the football. Daniel dropped the football. Daniel got the milk. Daniel took the apple. How many objects is Daniel holding? A: two Task 8: Lists/Sets Daniel picks up the football. Daniel drops the newspaper. Daniel picks up the milk. John took the apple. What is Daniel holding? milk, football | 1502.05698#16 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 16,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Task 3: Three Supporting Facts John picked up the apple. John went to the ofï¬ce. John went to the kitchen. John dropped the apple. Where was the apple before the kitchen? A:ofï¬ce Task 4: Two Argument Relations The ofï¬ce is north of the bedroom. The bedroom is north of the bathroom. The kitchen is west of the garden. What is north of the bedroom? A: ofï¬ce What is the bedroom north of? A: bathroom Task 5: Three Argument Relations Mary gave the cake to Fred. Fred gave the cake to Bill. Jeff was given the milk by Bill. Who gave the cake to Fred? A: Mary Who did Fred give the cake to? A: Bill Task 6: Yes/No Questions John moved to the playground. Daniel went to the bathroom. John went back to the hallway. Is John in the playground? A:no Is Daniel in the bathroom? A:yes Task 7: Counting Daniel picked up the football. Daniel dropped the football. Daniel got the milk. Daniel took the apple. How many objects is Daniel holding? A: two Task 8: Lists/Sets Daniel picks up the football. Daniel drops the newspaper. Daniel picks up the milk. John took the apple. What is Daniel holding? milk, football",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 17 | a. <a) aE, | Talalsn) y 0(4|$n)Adia($n+@) = Eaxg | q(4|sn) Aâ dora (Sns a] :
Our optimization problem in Equation (13) is exactly equivalent to the following one, written in terms of expec- tations:
To(als) qals) Qralss0)| (14) maximize Es~ po aoa~d
# 5.1 Single Path
In this estimation procedure, we collect a sequence of states by sampling s0 â¼ Ï0 and then simulating the pol- icy Ïθold for some number of timesteps to generate a trajec- tory s0, a0, s1, a1, . . . , sT â1, aT â1, sT . Hence, q(a|s) = Ïθold (a|s). Qθold (s, a) is computed at each state-action pair (st, at) by taking the discounted sum of future rewards along the trajectory.
# 5.2 Vine | 1502.05477#17 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 17,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "a. <a) aE, | Talalsn) y 0(4|$n)Adia($n+@) = Eaxg | q(4|sn) Aâ dora (Sns a] :\nOur optimization problem in Equation (13) is exactly equivalent to the following one, written in terms of expec- tations:\nTo(als) qals) Qralss0)| (14) maximize Es~ po aoa~d\n# 5.1 Single Path\nIn this estimation procedure, we collect a sequence of states by sampling s0 â¼ Ï0 and then simulating the pol- icy Ïθold for some number of timesteps to generate a trajec- tory s0, a0, s1, a1, . . . , sT â1, aT â1, sT . Hence, q(a|s) = Ïθold (a|s). Qθold (s, a) is computed at each state-action pair (st, at) by taking the discounted sum of future rewards along the trajectory.\n# 5.2 Vine",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05477 | 18 | # 5.2 Vine
In this estimation procedure, we ï¬rst sample s0 â¼ Ï0 and simulate the policy Ïθi to generate a number of trajecto- ries. We then choose a subset of N states along these tra- jectories, denoted s1, s2, . . . , sN , which we call the âroll- out setâ. For each state sn in the rollout set, we sample K actions according to an,k â¼ q(·|sn). Any choice of q(·|sn) with a support that includes the support of Ïθi(·|sn) will produce a consistent estimator. In practice, we found that q(·|sn) = Ïθi(·|sn) works well on continuous prob- lems, such as robotic locomotion, while the uniform dis- tribution works well on discrete tasks, such as the Atari games, where it can sometimes achieve better exploration.
subject to Es~py,,, [DL (Mra (-l8) || ma(-Is))] <4.
For each action an,k sampled at each state sn, we estiTrust Region Policy Optimization | 1502.05477#18 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 18,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "# 5.2 Vine\nIn this estimation procedure, we ï¬rst sample s0 â¼ Ï0 and simulate the policy Ïθi to generate a number of trajecto- ries. We then choose a subset of N states along these tra- jectories, denoted s1, s2, . . . , sN , which we call the âroll- out setâ. For each state sn in the rollout set, we sample K actions according to an,k â¼ q(·|sn). Any choice of q(·|sn) with a support that includes the support of Ïθi(·|sn) will produce a consistent estimator. In practice, we found that q(·|sn) = Ïθi(·|sn) works well on continuous prob- lems, such as robotic locomotion, while the uniform dis- tribution works well on discrete tasks, such as the Atari games, where it can sometimes achieve better exploration.\nsubject to Es~py,,, [DL (Mra (-l8) || ma(-Is))] <4.\nFor each action an,k sampled at each state sn, we estiTrust Region Policy Optimization",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 18 | Simple Negation and Indeï¬nite Knowledge Tasks 9 and 10 test slightly more complex natural language constructs. Task 9 tests one of the simplest forms of negation, that of supporting facts that imply a statement is false e.g. âFred is no longer in the ofï¬ceâ rather than âFred travelled to the ofï¬ceâ. (In this case, task 6 (yes/no questions) is a prerequisite to the task.) Task 10 tests if we can model statements that describe possibilities rather than certainties, e.g. âJohn is either in the classroom or the playground.â, where in that case the answer is âmaybeâ to the question âIs John in the classroom?â. | 1502.05698#18 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 18,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Simple Negation and Indeï¬nite Knowledge Tasks 9 and 10 test slightly more complex natural language constructs. Task 9 tests one of the simplest forms of negation, that of supporting facts that imply a statement is false e.g. âFred is no longer in the ofï¬ceâ rather than âFred travelled to the ofï¬ceâ. (In this case, task 6 (yes/no questions) is a prerequisite to the task.) Task 10 tests if we can model statements that describe possibilities rather than certainties, e.g. âJohn is either in the classroom or the playground.â, where in that case the answer is âmaybeâ to the question âIs John in the classroom?â.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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] |
1502.05477 | 19 | For each action an,k sampled at each state sn, we estiTrust Region Policy Optimization
mate ËQθi(sn, an,k) by performing a rollout (i.e., a short trajectory) starting with state sn and action an,k. We can greatly reduce the variance of the Q-value differences be- tween rollouts by using the same random number sequence for the noise in each of the K rollouts, i.e., common random numbers. See (Bertsekas, 2005) for additional discussion on Monte Carlo estimation of Q-values and (Ng & Jordan, 2000) for a discussion of common random numbers in re- inforcement learning.
In small, ï¬nite action spaces, we can generate a rollout for every possible action from a given state. The contribution to Lθold from a single state sn is as follows:
1. Use the single path or vine procedures to collect a set of state-action pairs along with Monte Carlo estimates of their Q-values.
2. By averaging over samples, construct the estimated objective and constraint in Equation (14).
3. Approximately solve this constrained optimization problem to update the policyâs parameter vector θ. We use the conjugate gradient algorithm followed by a line search, which is altogether only slightly more expensive than computing the gradient itself. See Ap- pendix C for details. | 1502.05477#19 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 19,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "For each action an,k sampled at each state sn, we estiTrust Region Policy Optimization\nmate ËQθi(sn, an,k) by performing a rollout (i.e., a short trajectory) starting with state sn and action an,k. We can greatly reduce the variance of the Q-value differences be- tween rollouts by using the same random number sequence for the noise in each of the K rollouts, i.e., common random numbers. See (Bertsekas, 2005) for additional discussion on Monte Carlo estimation of Q-values and (Ng & Jordan, 2000) for a discussion of common random numbers in re- inforcement learning.\nIn small, ï¬nite action spaces, we can generate a rollout for every possible action from a given state. The contribution to Lθold from a single state sn is as follows:\n1. Use the single path or vine procedures to collect a set of state-action pairs along with Monte Carlo estimates of their Q-values.\n2. By averaging over samples, construct the estimated objective and constraint in Equation (14).\n3. Approximately solve this constrained optimization problem to update the policyâs parameter vector θ. We use the conjugate gradient algorithm followed by a line search, which is altogether only slightly more expensive than computing the gradient itself. See Ap- pendix C for details.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 19 | Basic Coreference, Conjunctions and Compound Coreference Task 11 tests the simplest type of coreference, that of detecting the nearest referent, e.g. âDaniel was in the kitchen. Then he went to the studio.â. Real-world data typically addresses this as a labeling problem and studies more sophisticated phenomena (Soon et al., 2001), whereas we evaluate it as in all our other tasks as a question answering problem. Task 12 (conjunctions) tests referring to multiple subjects in a single statement, e.g. âMary and Jeff went to the kitchen.â. Task 13 tests coreference in the case where the pronoun can refer to multiple actors, e.g. âDaniel and Sandra journeyed to the ofï¬ce. Then they went to the gardenâ.
Time Reasoning While our tasks so far have included time implicitly in the order of the state- ments, task 14 tests understanding the use of time expressions within the statements, e.g. âIn the afternoon Julie went to the park. Yesterday Julie was at school.â, followed by questions about the order of events such as âWhere was Julie before the park?â. Real-world datasets address the task of evaluating time expressions typically as a labeling, rather than a QA task, see e.g. UzZaman et al. (2012). | 1502.05698#19 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 19,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Basic Coreference, Conjunctions and Compound Coreference Task 11 tests the simplest type of coreference, that of detecting the nearest referent, e.g. âDaniel was in the kitchen. Then he went to the studio.â. Real-world data typically addresses this as a labeling problem and studies more sophisticated phenomena (Soon et al., 2001), whereas we evaluate it as in all our other tasks as a question answering problem. Task 12 (conjunctions) tests referring to multiple subjects in a single statement, e.g. âMary and Jeff went to the kitchen.â. Task 13 tests coreference in the case where the pronoun can refer to multiple actors, e.g. âDaniel and Sandra journeyed to the ofï¬ce. Then they went to the gardenâ.\nTime Reasoning While our tasks so far have included time implicitly in the order of the state- ments, task 14 tests understanding the use of time expressions within the statements, e.g. âIn the afternoon Julie went to the park. Yesterday Julie was at school.â, followed by questions about the order of events such as âWhere was Julie before the park?â. Real-world datasets address the task of evaluating time expressions typically as a labeling, rather than a QA task, see e.g. UzZaman et al. (2012).",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 20 | = Smt ax |$n)Q (Sn, @k) (15)
where the action space is A = {a1,a2,...,ax}. In large or continuous state spaces, we can construct an estima- tor of the surrogate objective "Oaaa importance sampling. The self-normalized estimator (Owen| (2013), Chapter 9) of Lo... obtained at a single state s, is Dh et (Sn, an,k)
Dh et (Sn, an,k) Gora (An, k Ln (9) 9(analSa) (16) Dee 1 Fo (an,k18n)
assuming actions an,1, an,2, . . . , an,K from state sn. This self-normalized estimator removes the need to use a baseline for the Q-values (note that the gradient is unchanged by adding a constant to the Q-values). Averaging over sn â¼ Ï(Ï), we obtain an estimator for Lθold , as well as its gradient. | 1502.05477#20 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 20,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "= Smt ax |$n)Q (Sn, @k) (15)\nwhere the action space is A = {a1,a2,...,ax}. In large or continuous state spaces, we can construct an estima- tor of the surrogate objective \"Oaaa importance sampling. The self-normalized estimator (Owen| (2013), Chapter 9) of Lo... obtained at a single state s, is Dh et (Sn, an,k)\nDh et (Sn, an,k) Gora (An, k Ln (9) 9(analSa) (16) Dee 1 Fo (an,k18n)\nassuming actions an,1, an,2, . . . , an,K from state sn. This self-normalized estimator removes the need to use a baseline for the Q-values (note that the gradient is unchanged by adding a constant to the Q-values). Averaging over sn â¼ Ï(Ï), we obtain an estimator for Lθold , as well as its gradient.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05477 | 21 | With regard to (3), we construct the Fisher informa- tion matrix (FIM) by analytically computing the Hessian of the KL divergence, rather than using the covariance matrix of the gradients. That is, we estimate A;; as N 2 W on=1 O80; Dxu (Tra (-|Sn) )), rather than * ee on log To(an|Sn) go; log 79(@n|$n) lytic estimator integrates over the action at each state s,,, and does not depend on the action a, that was sampled. As described in Appendix |C} this analytic estimator has computational benefits in the large-scale setting, since it removes the need to store a dense Hessian or all policy gra- dients from a batch of trajectories. The rate of improvement in the policy is similar to the empirical FIM, as shown in the experiments. Il 70(-|Sn The anaLet us brieï¬y summarize the relationship between the the- ory from Section 3 and the practical algorithm we have de- scribed:
The vine and single path methods are illustrated in Figure 1. We use the term vine, since the trajectories used for sam- pling can be likened to the stems of vines, which branch at various points (the rollout set) into several short offshoots (the rollout trajectories). | 1502.05477#21 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 21,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "With regard to (3), we construct the Fisher informa- tion matrix (FIM) by analytically computing the Hessian of the KL divergence, rather than using the covariance matrix of the gradients. That is, we estimate A;; as N 2 W on=1 O80; Dxu (Tra (-|Sn) )), rather than * ee on log To(an|Sn) go; log 79(@n|$n) lytic estimator integrates over the action at each state s,,, and does not depend on the action a, that was sampled. As described in Appendix |C} this analytic estimator has computational benefits in the large-scale setting, since it removes the need to store a dense Hessian or all policy gra- dients from a batch of trajectories. The rate of improvement in the policy is similar to the empirical FIM, as shown in the experiments. Il 70(-|Sn The anaLet us brieï¬y summarize the relationship between the the- ory from Section 3 and the practical algorithm we have de- scribed:\nThe vine and single path methods are illustrated in Figure 1. We use the term vine, since the trajectories used for sam- pling can be likened to the stems of vines, which branch at various points (the rollout set) into several short offshoots (the rollout trajectories).",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 21 | Task 11: Basic Coreference Daniel was in the kitchen. Then he went to the studio. Sandra was in the ofï¬ce. Where is Daniel? A:studio Task 12: Conjunction Mary and Jeff went to the kitchen. Then Jeff went to the park. Where is Mary? A: kitchen Where is Jeff? A: park Task 13: Compound Coreference Daniel and Sandra journeyed to the ofï¬ce. Then they went to the garden. Sandra and John travelled to the kitchen. After that they moved to the hallway. Where is Daniel? A: garden Task 14: Time Reasoning In the afternoon Julie went to the park. Yesterday Julie was at school. Julie went to the cinema this evening. Where did Julie go after the park? A:cinema Where was Julie before the park? A:school Task 15: Basic Deduction Sheep are afraid of wolves. Cats are afraid of dogs. Mice are afraid of cats. Gertrude is a sheep. What is Gertrude afraid of? A:wolves Task 16: Basic Induction Lily is a swan. Lily is white. Bernhard is green. Greg is a swan. What color is Greg? A:white Task 17: Positional Reasoning The triangle is | 1502.05698#21 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 21,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Task 11: Basic Coreference Daniel was in the kitchen. Then he went to the studio. Sandra was in the ofï¬ce. Where is Daniel? A:studio Task 12: Conjunction Mary and Jeff went to the kitchen. Then Jeff went to the park. Where is Mary? A: kitchen Where is Jeff? A: park Task 13: Compound Coreference Daniel and Sandra journeyed to the ofï¬ce. Then they went to the garden. Sandra and John travelled to the kitchen. After that they moved to the hallway. Where is Daniel? A: garden Task 14: Time Reasoning In the afternoon Julie went to the park. Yesterday Julie was at school. Julie went to the cinema this evening. Where did Julie go after the park? A:cinema Where was Julie before the park? A:school Task 15: Basic Deduction Sheep are afraid of wolves. Cats are afraid of dogs. Mice are afraid of cats. Gertrude is a sheep. What is Gertrude afraid of? A:wolves Task 16: Basic Induction Lily is a swan. Lily is white. Bernhard is green. Greg is a swan. What color is Greg? A:white Task 17: Positional Reasoning The triangle is",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 22 | The beneï¬t of the vine method over the single path method that is our local estimate of the objective has much lower variance given the same number of Q-value samples in the surrogate objective. That is, the vine method gives much better estimates of the advantage values. The downside of the vine method is that we must perform far more calls to the simulator for each of these advantage estimates. Fur- thermore, the vine method requires us to generate multiple trajectories from each state in the rollout set, which limits this algorithm to settings where the system can be reset to an arbitrary state. In contrast, the single path algorithm re- quires no state resets and can be directly implemented on a physical system (Peters & Schaal, 2008b).
⢠The theory justiï¬es optimizing a surrogate objective with a penalty on KL divergence. However, the large penalty coefï¬cient C leads to prohibitively small steps, so we would like to decrease this coefï¬cient. Empirically, it is hard to robustly choose the penalty coefï¬cient, so we use a hard constraint instead of a penalty, with parameter δ (the bound on KL diver- gence).
KL (θold, θ) is hard for numerical optimization and estimation, so instead we constrain DKL(θold, θ). | 1502.05477#22 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 22,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "The beneï¬t of the vine method over the single path method that is our local estimate of the objective has much lower variance given the same number of Q-value samples in the surrogate objective. That is, the vine method gives much better estimates of the advantage values. The downside of the vine method is that we must perform far more calls to the simulator for each of these advantage estimates. Fur- thermore, the vine method requires us to generate multiple trajectories from each state in the rollout set, which limits this algorithm to settings where the system can be reset to an arbitrary state. In contrast, the single path algorithm re- quires no state resets and can be directly implemented on a physical system (Peters & Schaal, 2008b).\n⢠The theory justiï¬es optimizing a surrogate objective with a penalty on KL divergence. However, the large penalty coefï¬cient C leads to prohibitively small steps, so we would like to decrease this coefï¬cient. Empirically, it is hard to robustly choose the penalty coefï¬cient, so we use a hard constraint instead of a penalty, with parameter δ (the bound on KL diver- gence).\nKL (θold, θ) is hard for numerical optimization and estimation, so instead we constrain DKL(θold, θ).",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 22 | is a swan. Lily is white. Bernhard is green. Greg is a swan. What color is Greg? A:white Task 17: Positional Reasoning The triangle is to the right of the blue square. The red square is on top of the blue square. The red sphere is to the right of the blue square. Is the red sphere to the right of the blue square? A:yes Is the red square to the left of the triangle? A:yes Task 18: Size Reasoning The football ï¬ts in the suitcase. The suitcase ï¬ts in the cupboard. The box is smaller than the football. Will the box ï¬t in the suitcase? A:yes Will the cupboard ï¬t in the box? A:no | 1502.05698#22 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 22,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "is a swan. Lily is white. Bernhard is green. Greg is a swan. What color is Greg? A:white Task 17: Positional Reasoning The triangle is to the right of the blue square. The red square is on top of the blue square. The red sphere is to the right of the blue square. Is the red sphere to the right of the blue square? A:yes Is the red square to the left of the triangle? A:yes Task 18: Size Reasoning The football ï¬ts in the suitcase. The suitcase ï¬ts in the cupboard. The box is smaller than the football. Will the box ï¬t in the suitcase? A:yes Will the cupboard ï¬t in the box? A:no",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 23 | KL (θold, θ) is hard for numerical optimization and estimation, so instead we constrain DKL(θold, θ).
⢠Our theory ignores estimation error for the advantage function. Kakade & Langford (2002) consider this er- ror in their derivation, and the same arguments would hold in the setting of this paper, but we omit them for simplicity.
# 6 Practical Algorithm
Here we present two practical policy optimization algo- rithm based on the ideas above, which use either the single path or vine sampling scheme from the preceding section. The algorithms repeatedly perform the following steps:
# 7 Connections with Prior Work
As mentioned in Section 4, our derivation results in a pol- icy update that is related to several prior methods, provid- ing a unifying perspective on a number of policy update
Optimization
Trust Region Policy Optimization
schemes. The natural policy gradient (Kakade, 2002) can be obtained as a special case of the update in Equation (12) by using a linear approximation to L and a quadratic ap- proximation to the DKL constraint, resulting in the follow- ing problem:
maximize [Volo loonyâ (O- dora) aâ
# [Volo 5 (Gaia - 0)" | 1502.05477#23 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 23,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "KL (θold, θ) is hard for numerical optimization and estimation, so instead we constrain DKL(θold, θ).\n⢠Our theory ignores estimation error for the advantage function. Kakade & Langford (2002) consider this er- ror in their derivation, and the same arguments would hold in the setting of this paper, but we omit them for simplicity.\n# 6 Practical Algorithm\nHere we present two practical policy optimization algo- rithm based on the ideas above, which use either the single path or vine sampling scheme from the preceding section. The algorithms repeatedly perform the following steps:\n# 7 Connections with Prior Work\nAs mentioned in Section 4, our derivation results in a pol- icy update that is related to several prior methods, provid- ing a unifying perspective on a number of policy update\nOptimization\nTrust Region Policy Optimization\nschemes. The natural policy gradient (Kakade, 2002) can be obtained as a special case of the update in Equation (12) by using a linear approximation to L and a quadratic ap- proximation to the DKL constraint, resulting in the follow- ing problem:\nmaximize [Volo loonyâ (O- dora) aâ\n# [Volo 5 (Gaia - 0)\"",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 23 | tests basic induction via inheritance of properties. A full analysis of induction and deduction is clearly beyond the scope of this work, and future tasks should analyse further, deeper aspects.
Positional and Size Reasoning Task 17 tests spatial reasoning, one of many components of the classical SHRDLU system (Winograd, 1972) by asking questions about the relative positions of colored blocks. Task 18 requires reasoning about the relative size of objects and is inspired by the commonsense reasoning examples in the Winograd schema challenge (Levesque et al., 2011).
Path Finding The goal of task 19 is to ï¬nd the path between locations: given the description of various locations, it asks: how do you get from one to another? This is related to the work of Chen & Mooney (2011) and effectively involves a search problem.
Agentâs Motivations Finally, task 20 questions, in the simplest way possible, why an agent per- forms an action. It addresses the case of actors being in a given state (hungry, thirsty, tired, . . . ) and the actions they then take, e.g. it should learn that hungry people might go to the kitchen, and so on.
As already stated, these tasks are meant to foster the development and understanding of machine learning algorithms. A single model should be evaluated across all the tasks (not tuning per task) and then the same model should be tested on additional real-world tasks. | 1502.05698#23 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 23,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "tests basic induction via inheritance of properties. A full analysis of induction and deduction is clearly beyond the scope of this work, and future tasks should analyse further, deeper aspects.\nPositional and Size Reasoning Task 17 tests spatial reasoning, one of many components of the classical SHRDLU system (Winograd, 1972) by asking questions about the relative positions of colored blocks. Task 18 requires reasoning about the relative size of objects and is inspired by the commonsense reasoning examples in the Winograd schema challenge (Levesque et al., 2011).\nPath Finding The goal of task 19 is to ï¬nd the path between locations: given the description of various locations, it asks: how do you get from one to another? This is related to the work of Chen & Mooney (2011) and effectively involves a search problem.\nAgentâs Motivations Finally, task 20 questions, in the simplest way possible, why an agent per- forms an action. It addresses the case of actors being in a given state (hungry, thirsty, tired, . . . ) and the actions they then take, e.g. it should learn that hungry people might go to the kitchen, and so on.\nAs already stated, these tasks are meant to foster the development and understanding of machine learning algorithms. A single model should be evaluated across all the tasks (not tuning per task) and then the same model should be tested on additional real-world tasks.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 24 | maximize [Volo loonyâ (O- dora) aâ
# [Volo 5 (Gaia - 0)"
(θold â θ)T A(θold)(θold â θ) ⤠δ,
Figure 2. 2D robot models used for locomotion experiments. From left to right: swimmer, hopper, walker. The hopper and walker present a particular challenge, due to underactuation and contact discontinuities.
# subject to
where A(θold)ij =
00 30, 00, B= [Diu (a(-|8, Bora) || 715; 9))] [og oldâ
Input layer Fully connected layer Mean parameters Sampling â vr Control Standard deviations 30 units Input Cony, Cony. Hidden Action layer layer layer layer probabilities Sampling Control Screen input 16 filters 16 filters 20 units
The update is Orew = 9oia + ZA ota) *VoL(9)|5-6.,,> where the stepsize + is typically treated as an algorithm parameter. This differs from our approach, which en- forces the constraint at each update. Though this difference might seem subtle, our experiments demonstrate that it sig- nificantly improves the algorithmâs performance on larger problems. | 1502.05477#24 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 24,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "maximize [Volo loonyâ (O- dora) aâ\n# [Volo 5 (Gaia - 0)\"\n(θold â θ)T A(θold)(θold â θ) ⤠δ,\nFigure 2. 2D robot models used for locomotion experiments. From left to right: swimmer, hopper, walker. The hopper and walker present a particular challenge, due to underactuation and contact discontinuities.\n# subject to\nwhere A(θold)ij =\n00 30, 00, B= [Diu (a(-|8, Bora) || 715; 9))] [og oldâ\nInput layer Fully connected layer Mean parameters Sampling â vr Control Standard deviations 30 units Input Cony, Cony. Hidden Action layer layer layer layer probabilities Sampling Control Screen input 16 filters 16 filters 20 units\nThe update is Orew = 9oia + ZA ota) *VoL(9)|5-6.,,> where the stepsize + is typically treated as an algorithm parameter. This differs from our approach, which en- forces the constraint at each update. Though this difference might seem subtle, our experiments demonstrate that it sig- nificantly improves the algorithmâs performance on larger problems.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 24 | In our data release, in addition to providing the above 20 tasks in English, we also provide them (i) in Hindi; and (ii) with shufï¬ed English words so they are no longer readable by humans. A good learning algorithm should perform similarly on all three, which would likely not be the case for a method using external resources, a setting intended to mimic a learner being ï¬rst presented a language and having to learn from scratch.
5
# Under review as a conference paper at ICLR 2016
# 4 SIMULATION
All our tasks are generated with a simulation which behaves like a classic text adventure game. The idea is that generating text within this simulation allows us to ground the language used into a coherent and controlled (artiï¬cial) world. Our simulation follows those of Bordes et al. (2010); Weston et al. (2014) but is somewhat more complex. | 1502.05698#24 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 24,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "In our data release, in addition to providing the above 20 tasks in English, we also provide them (i) in Hindi; and (ii) with shufï¬ed English words so they are no longer readable by humans. A good learning algorithm should perform similarly on all three, which would likely not be the case for a method using external resources, a setting intended to mimic a learner being ï¬rst presented a language and having to learn from scratch.\n5\n# Under review as a conference paper at ICLR 2016\n# 4 SIMULATION\nAll our tasks are generated with a simulation which behaves like a classic text adventure game. The idea is that generating text within this simulation allows us to ground the language used into a coherent and controlled (artiï¬cial) world. Our simulation follows those of Bordes et al. (2010); Weston et al. (2014) but is somewhat more complex.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 25 | We can also obtain the standard policy gradient update by using an > constraint or penalty: maximize [VoLeae()looon (O- Gora)| (18) 1 subject to 3 le â Ooral|? < 6.
The policy iteration update can also be obtained by solving the unconstrained problem maximizeÏ LÏold(Ï), using L as deï¬ned in Equation (3).
Figure 3. Neural networks used for the locomotion task (top) and for playing Atari games (bottom).
3. Can TRPO be used to solve challenging large-scale problems? How does TRPO compare with other methods when applied to large-scale problems, with regard to ï¬nal performance, computation time, and sample complexity? | 1502.05477#25 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 25,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "We can also obtain the standard policy gradient update by using an > constraint or penalty: maximize [VoLeae()looon (O- Gora)| (18) 1 subject to 3 le â Ooral|? < 6.\nThe policy iteration update can also be obtained by solving the unconstrained problem maximizeÏ LÏold(Ï), using L as deï¬ned in Equation (3).\nFigure 3. Neural networks used for the locomotion task (top) and for playing Atari games (bottom).\n3. Can TRPO be used to solve challenging large-scale problems? How does TRPO compare with other methods when applied to large-scale problems, with regard to ï¬nal performance, computation time, and sample complexity?",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 25 | The simulated world is composed of entities of various types (locations, objects, persons. etc.) and of various actions that operate on these entities. Entities have internal states: their location, whether they carry objects on top or inside them (e.g., tables and boxes), the mental state of actors (e.g. hungry), as well as properties such as size, color, and edibility. For locations, the nearby places that are connected (e.g. what lies to the east, or above) are encoded. For actors, a set of pre-speciï¬ed rules per actor can also be speciï¬ed to control their behavior, e.g. if they are hungry they may try to ï¬nd food. Random valid actions can also be executed if no rule is set, e.g. walking around randomly. | 1502.05698#25 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 25,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "The simulated world is composed of entities of various types (locations, objects, persons. etc.) and of various actions that operate on these entities. Entities have internal states: their location, whether they carry objects on top or inside them (e.g., tables and boxes), the mental state of actors (e.g. hungry), as well as properties such as size, color, and edibility. For locations, the nearby places that are connected (e.g. what lies to the east, or above) are encoded. For actors, a set of pre-speciï¬ed rules per actor can also be speciï¬ed to control their behavior, e.g. if they are hungry they may try to ï¬nd food. Random valid actions can also be executed if no rule is set, e.g. walking around randomly.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 26 | Several other methods employ an update similar to Equa- tion (12). Relative entropy policy search (REPS) (Peters et al., 2010) constrains the state-action marginals p(s, a), while TRPO constrains the conditionals p(a|s). Unlike REPS, our approach does not require a costly nonlinear op- timization in the inner loop. Levine and Abbeel (2014) also use a KL divergence constraint, but its purpose is to encour- age the policy not to stray from regions where the estimated dynamics model is valid, while we do not attempt to esti- mate the system dynamics explicitly. Pirotta et al. (2013) also build on and generalize Kakade and Langfordâs results, and they derive different algorithms from the ones here.
To answer (1) and (2), we compare the performance of the single path and vine variants of TRPO, several ablated variants, and a number of prior policy optimization algo- rithms. With regard to (3), we show that both the single path and vine algorithm can obtain high-quality locomo- tion controllers from scratch, which is considered to be a hard problem. We also show that these algorithms produce competitive results when learning policies for playing Atari games from images using convolutional neural networks with tens of thousands of parameters.
# 8 Experiments
We designed our experiments to investigate the following questions: | 1502.05477#26 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 26,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Several other methods employ an update similar to Equa- tion (12). Relative entropy policy search (REPS) (Peters et al., 2010) constrains the state-action marginals p(s, a), while TRPO constrains the conditionals p(a|s). Unlike REPS, our approach does not require a costly nonlinear op- timization in the inner loop. Levine and Abbeel (2014) also use a KL divergence constraint, but its purpose is to encour- age the policy not to stray from regions where the estimated dynamics model is valid, while we do not attempt to esti- mate the system dynamics explicitly. Pirotta et al. (2013) also build on and generalize Kakade and Langfordâs results, and they derive different algorithms from the ones here.\nTo answer (1) and (2), we compare the performance of the single path and vine variants of TRPO, several ablated variants, and a number of prior policy optimization algo- rithms. With regard to (3), we show that both the single path and vine algorithm can obtain high-quality locomo- tion controllers from scratch, which is considered to be a hard problem. We also show that these algorithms produce competitive results when learning policies for playing Atari games from images using convolutional neural networks with tens of thousands of parameters.\n# 8 Experiments\nWe designed our experiments to investigate the following questions:",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 26 | The actions an actor can execute in the simulation consist of the following: go <location>, get <object>, get <object1> from <object2>, put <object1> in/on <object2>, give <object> to <actor>, drop <object>, set <entitity> <state>, look, inventory and examine <object>. A set of universal constraints is imposed on those actions to enforce coherence in the simulation. For example an actor cannot get something that they or someone else already has, they cannot go to a place that is not connected to the current location, cannot drop something they do not already have, and so on. Using the underlying actions, rules for actors, and their constraints, deï¬nes how actors act. For each task we limit the actions needed for that task, e.g. task 1 only needs go whereas task 2 uses go, get and drop. If we write the commands down this gives us a very simple âstoryâ which is executable by the simulation, e.g., joe go playground; bob go ofï¬ce; joe get football. This example corresponds to task 2. The system can then ask questions about the state of the simulation e.g., where john?, where football? and so on. It is easy to calculate the true answers for these questions as we have access to the underlying world. | 1502.05698#26 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 26,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "The actions an actor can execute in the simulation consist of the following: go <location>, get <object>, get <object1> from <object2>, put <object1> in/on <object2>, give <object> to <actor>, drop <object>, set <entitity> <state>, look, inventory and examine <object>. A set of universal constraints is imposed on those actions to enforce coherence in the simulation. For example an actor cannot get something that they or someone else already has, they cannot go to a place that is not connected to the current location, cannot drop something they do not already have, and so on. Using the underlying actions, rules for actors, and their constraints, deï¬nes how actors act. For each task we limit the actions needed for that task, e.g. task 1 only needs go whereas task 2 uses go, get and drop. If we write the commands down this gives us a very simple âstoryâ which is executable by the simulation, e.g., joe go playground; bob go ofï¬ce; joe get football. This example corresponds to task 2. The system can then ask questions about the state of the simulation e.g., where john?, where football? and so on. It is easy to calculate the true answers for these questions as we have access to the underlying world.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 27 | # 8 Experiments
We designed our experiments to investigate the following questions:
# 8.1 Simulated Robotic Locomotion
1. What are the performance characteristics of the single path and vine sampling procedures?
We conducted the robotic locomotion experiments using the MuJoCo simulator (Todorov et al., 2012). The three simulated robots are shown in Figure 2. The states of the robots are their generalized positions and velocities, and the controls are joint torques. Underactuation, high dimension- ality, and non-smooth dynamics due to contacts make these
2. TRPO is related to prior methods (e.g. natural policy gradient) but makes several changes, most notably by using a ï¬xed KL divergence rather than a ï¬xed penalty coefï¬cient. How does this affect the performance of the algorithm?
Trust Region Policy Optimization
tasks very challenging. The following models are included in our evaluation:
1. Swimmer. 10-dimensional state space, linear reward for forward progress and a quadratic penalty on joint effort to produce the reward r(x, u) = veâ107>||ull?. The swimmer can propel itself forward by making an undulating motion. | 1502.05477#27 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 27,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "# 8 Experiments\nWe designed our experiments to investigate the following questions:\n# 8.1 Simulated Robotic Locomotion\n1. What are the performance characteristics of the single path and vine sampling procedures?\nWe conducted the robotic locomotion experiments using the MuJoCo simulator (Todorov et al., 2012). The three simulated robots are shown in Figure 2. The states of the robots are their generalized positions and velocities, and the controls are joint torques. Underactuation, high dimension- ality, and non-smooth dynamics due to contacts make these\n2. TRPO is related to prior methods (e.g. natural policy gradient) but makes several changes, most notably by using a ï¬xed KL divergence rather than a ï¬xed penalty coefï¬cient. How does this affect the performance of the algorithm?\nTrust Region Policy Optimization\ntasks very challenging. The following models are included in our evaluation:\n1. Swimmer. 10-dimensional state space, linear reward for forward progress and a quadratic penalty on joint effort to produce the reward r(x, u) = veâ107>||ull?. The swimmer can propel itself forward by making an undulating motion.",
"title": "Trust Region Policy Optimization",
"year": 2015
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1502.05698 | 27 | To produce more natural looking text with lexical variety from statements and questions we employ a simple automated grammar. Each verb is assigned a set of synonyms, e.g., the simulation command get is replaced with either picked up, got, grabbed or took, and drop is replaced with either dropped, left, discarded or put down. Similarly, each object and actor can have a set of replacement synonyms as well, e.g. replacing Daniel with he in task 11. Adverbs are crucial for some tasks such as the time reasoning task 14.
There are a great many aspects of language not yet modeled. For example, all sentences are so far relatively short and contain little nesting. Further, the entities and the vocabulary size is small (150 words, and typically 4 actors, 6 locations and 3 objects used per task). The hope is that deï¬ning a set of well deï¬ned tasks will help evaluate models in a controlled way within the simulated environment, which is hard to do with real data. That is, these tasks are not a substitute for real data, but should complement them, especially when developing and analysing algorithms.
# 5 EXPERIMENTS | 1502.05698#27 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 27,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "To produce more natural looking text with lexical variety from statements and questions we employ a simple automated grammar. Each verb is assigned a set of synonyms, e.g., the simulation command get is replaced with either picked up, got, grabbed or took, and drop is replaced with either dropped, left, discarded or put down. Similarly, each object and actor can have a set of replacement synonyms as well, e.g. replacing Daniel with he in task 11. Adverbs are crucial for some tasks such as the time reasoning task 14.\nThere are a great many aspects of language not yet modeled. For example, all sentences are so far relatively short and contain little nesting. Further, the entities and the vocabulary size is small (150 words, and typically 4 actors, 6 locations and 3 objects used per task). The hope is that deï¬ning a set of well deï¬ned tasks will help evaluate models in a controlled way within the simulated environment, which is hard to do with real data. That is, these tasks are not a substitute for real data, but should complement them, especially when developing and analysing algorithms.\n# 5 EXPERIMENTS",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 28 | 2. Hopper. 12-dimensional state space, same reward as the swimmer, with a bonus of +1 for being in a non- terminal state. We ended the episodes when the hop- per fell over, which was deï¬ned by thresholds on the torso height and angle.
Cartpole we Swimmer of policy 3%: walker
3. Walker. 18-dimensional state space. For the walker, we added a penalty for strong impacts of the feet against the ground to encourage a smooth walk rather than a hopping gait.
We used δ = 0.01 for all experiments. See Table 2 in the Appendix for more details on the experimental setup and parameters used. We used neural networks to represent the policy, with the architecture shown in Figure 3, and further details provided in Appendix D. To establish a standard baseline, we also included the classic cart-pole balancing problem, based on the formulation from Barto et al. (1983), using a linear policy with six parameters that is easy to opti- mize with derivative-free black-box optimization methods. | 1502.05477#28 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 28,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "2. Hopper. 12-dimensional state space, same reward as the swimmer, with a bonus of +1 for being in a non- terminal state. We ended the episodes when the hop- per fell over, which was deï¬ned by thresholds on the torso height and angle.\nCartpole we Swimmer of policy 3%: walker\n3. Walker. 18-dimensional state space. For the walker, we added a penalty for strong impacts of the feet against the ground to encourage a smooth walk rather than a hopping gait.\nWe used δ = 0.01 for all experiments. See Table 2 in the Appendix for more details on the experimental setup and parameters used. We used neural networks to represent the policy, with the architecture shown in Figure 3, and further details provided in Appendix D. To establish a standard baseline, we also included the classic cart-pole balancing problem, based on the formulation from Barto et al. (1983), using a linear policy with six parameters that is easy to opti- mize with derivative-free black-box optimization methods.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 28 | # 5 EXPERIMENTS
We compared the following methods on our tasks (on the English dataset): (i) an N - gram classiï¬er baseline, (ii) LSTMs (long short term memory Recurrent Neural Networks) (Hochreiter & Schmidhuber, 1997), (iii) Memory Networks (MemNNs) (Weston et al., 2014), (iv) some extensions of Memory Networks we will detail; and (v) a structured SVM that incorporates external labeled data from existing NLP tasks. These models belong to three separate tracks. Weakly supervised models are only given question answer pairs at training time, whereas strong supervision provides the set of supporting facts at training time (but not testing time) as well. Strongly super- vised ones give accuracy upper bounds for weakly supervised models, i.e. the performance should be superior given the same model class. Methods in the last external resources track can use labeled data from other sources rather than just the training set provided, e.g. coreference and semantic role labeling tasks, as well as strong supervision. For each task we use 1000 questions for training, and 1000 for testing, and report the test accuracy. We consider a task successfully passed if ⥠95% accuracy is obtained3.
3The choice of 95% (and 1000 training examples) is arbitrary.
6
# Under review as a conference paper at ICLR 2016 | 1502.05698#28 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 28,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "# 5 EXPERIMENTS\nWe compared the following methods on our tasks (on the English dataset): (i) an N - gram classiï¬er baseline, (ii) LSTMs (long short term memory Recurrent Neural Networks) (Hochreiter & Schmidhuber, 1997), (iii) Memory Networks (MemNNs) (Weston et al., 2014), (iv) some extensions of Memory Networks we will detail; and (v) a structured SVM that incorporates external labeled data from existing NLP tasks. These models belong to three separate tracks. Weakly supervised models are only given question answer pairs at training time, whereas strong supervision provides the set of supporting facts at training time (but not testing time) as well. Strongly super- vised ones give accuracy upper bounds for weakly supervised models, i.e. the performance should be superior given the same model class. Methods in the last external resources track can use labeled data from other sources rather than just the training set provided, e.g. coreference and semantic role labeling tasks, as well as strong supervision. For each task we use 1000 questions for training, and 1000 for testing, and report the test accuracy. We consider a task successfully passed if ⥠95% accuracy is obtained3.\n3The choice of 95% (and 1000 training examples) is arbitrary.\n6\n# Under review as a conference paper at ICLR 2016",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 29 | The following algorithms were considered in the compari- son: single path TRPO; vine TRPO; cross-entropy method (CEM), a gradient-free method (Szita & L¨orincz, 2006); covariance matrix adaption (CMA), another gradient-free method (Hansen & Ostermeier, 1996); natural gradi- ent, the classic natural policy gradient algorithm (Kakade, 2002), which differs from single path by the use of a ï¬xed penalty coefï¬cient (Lagrange multiplier) instead of the KL divergence constraint; empirical FIM, identical to single path, except that the FIM is estimated using the covariance matrix of the gradients rather than the analytic estimate; max KL, which was only tractable on the cart-pole problem, and uses the maximum KL divergence in Equation (11), rather than the average divergence, allowing us to evaluate the quality of this approximation. The parameters used in the experiments are provided in Appendix E. For the natu- ral gradient method, we swept through the possible values of the stepsize in factors of three, and took the best value according to the ï¬nal performance. | 1502.05477#29 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 29,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "The following algorithms were considered in the compari- son: single path TRPO; vine TRPO; cross-entropy method (CEM), a gradient-free method (Szita & L¨orincz, 2006); covariance matrix adaption (CMA), another gradient-free method (Hansen & Ostermeier, 1996); natural gradi- ent, the classic natural policy gradient algorithm (Kakade, 2002), which differs from single path by the use of a ï¬xed penalty coefï¬cient (Lagrange multiplier) instead of the KL divergence constraint; empirical FIM, identical to single path, except that the FIM is estimated using the covariance matrix of the gradients rather than the analytic estimate; max KL, which was only tractable on the cart-pole problem, and uses the maximum KL divergence in Equation (11), rather than the average divergence, allowing us to evaluate the quality of this approximation. The parameters used in the experiments are provided in Appendix E. For the natu- ral gradient method, we swept through the possible values of the stepsize in factors of three, and took the best value according to the ï¬nal performance.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 29 | 3The choice of 95% (and 1000 training examples) is arbitrary.
6
# Under review as a conference paper at ICLR 2016
Table 3: Test accuracy (%) on our 20 Tasks for various methods (1000 training examples each). Our proposed extensions to MemNNs are in columns 5-9: with adaptive memory (AM), N -grams (NG), nonlinear matching function (NL), and combinations thereof. Bold numbers indicate tasks where our extensions achieve ⥠95% accuracy but the original MemNN model of Weston et al. (2014) did not. The last two columns (10-11) give method. Column 10 gives the amount of training data for each task needed to extra analysis of the MemNN AM + NG + NL
obtain ⥠95% accuracy, or FAIL if this is not achievable with 1000 training examples. The ï¬nal column gives the accuracy when training on all data at once, rather than separately. | 1502.05698#29 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 29,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "3The choice of 95% (and 1000 training examples) is arbitrary.\n6\n# Under review as a conference paper at ICLR 2016\nTable 3: Test accuracy (%) on our 20 Tasks for various methods (1000 training examples each). Our proposed extensions to MemNNs are in columns 5-9: with adaptive memory (AM), N -grams (NG), nonlinear matching function (NL), and combinations thereof. Bold numbers indicate tasks where our extensions achieve ⥠95% accuracy but the original MemNN model of Weston et al. (2014) did not. The last two columns (10-11) give method. Column 10 gives the amount of training data for each task needed to extra analysis of the MemNN AM + NG + NL\nobtain ⥠95% accuracy, or FAIL if this is not achievable with 1000 training examples. The ï¬nal column gives the accuracy when training on all data at once, rather than separately.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 30 | Learning curves showing the total reward averaged across ï¬ve runs of each algorithm are shown in Figure 4. Single path and vine TRPO solved all of the problems, yielding the best solutions. Natural gradient performed well on the two easier problems, but was unable to generate hopping and walking gaits that made forward progress. These re- sults provide empirical evidence that constraining the KL divergence is a more robust way to choose step sizes and make fast, consistent progress, compared to using a ï¬xed
Figure 4. Learning curves for locomotion tasks, averaged across ï¬ve runs of each algorithm with random initializations. Note that 1 is achievable without any for the hopper and walker, a score of forward velocity, indicating a policy that simply learned balanced standing, but not walking.
penalty. CEM and CMA are derivative-free algorithms, hence their sample complexity scales unfavorably with the number of parameters, and they performed poorly on the larger problems. The max KL method learned somewhat more slowly than our ï¬nal method, due to the more restric- tive form of the constraint, but overall the result suggests that the average KL divergence constraint has a similar ef- fect as the theorecally justiï¬ed maximum KL divergence. Videos of the policies learned by TRPO may be viewed on the project website: http://sites.google.com/ site/trpopaper/. | 1502.05477#30 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 30,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Learning curves showing the total reward averaged across ï¬ve runs of each algorithm are shown in Figure 4. Single path and vine TRPO solved all of the problems, yielding the best solutions. Natural gradient performed well on the two easier problems, but was unable to generate hopping and walking gaits that made forward progress. These re- sults provide empirical evidence that constraining the KL divergence is a more robust way to choose step sizes and make fast, consistent progress, compared to using a ï¬xed\nFigure 4. Learning curves for locomotion tasks, averaged across ï¬ve runs of each algorithm with random initializations. Note that 1 is achievable without any for the hopper and walker, a score of forward velocity, indicating a policy that simply learned balanced standing, but not walking.\npenalty. CEM and CMA are derivative-free algorithms, hence their sample complexity scales unfavorably with the number of parameters, and they performed poorly on the larger problems. The max KL method learned somewhat more slowly than our ï¬nal method, due to the more restric- tive form of the constraint, but overall the result suggests that the average KL divergence constraint has a similar ef- fect as the theorecally justiï¬ed maximum KL divergence. Videos of the policies learned by TRPO may be viewed on the project website: http://sites.google.com/ site/trpopaper/.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 30 | Weakly Supervised Uses External Resources Strong Supervision (using supporting facts) TASK 1 - Single Supporting Fact 2 - Two Supporting Facts 3 - Three Supporting Facts 4 - Two Arg. Relations 5 - Three Arg. Relations 6 - Yes/No Questions 7 - Counting 8 - Lists/Sets 9 - Simple Negation 10 - Indeï¬nite Knowledge 11 - Basic Coreference 12 - Conjunction 13 - Compound Coref. 14 - Time Reasoning 15 - Basic Deduction 16 - Basic Induction 17 - Positional Reasoning 18 - Size Reasoning 19 - Path Finding 20 - Agentâs Motivations Mean Performance M SV features +SRL 95 ⥠req. ex. of No. RY N O N M E Mem M E PTIV DA A 100 100 100 69 83 52 78 90 71 57 100 100 100 100 73 100 46 50 9 100 79 (2014) N N Mem al. et Weston 100 100 20 71 83 47 68 77 65 59 100 100 100 99 74 27 54 57 0 100 75 R A E N N NLIN O N + Structured N MS N A R N-G + N NL N + G N + N-gram Classiï¬er M LST Mem Mem Mem F E R O C M A 100 100 100 73 86 100 83 94 100 97 100 100 100 100 77 100 57 54 15 100 87 | 1502.05698#30 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 30,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Weakly Supervised Uses External Resources Strong Supervision (using supporting facts) TASK 1 - Single Supporting Fact 2 - Two Supporting Facts 3 - Three Supporting Facts 4 - Two Arg. Relations 5 - Three Arg. Relations 6 - Yes/No Questions 7 - Counting 8 - Lists/Sets 9 - Simple Negation 10 - Indeï¬nite Knowledge 11 - Basic Coreference 12 - Conjunction 13 - Compound Coref. 14 - Time Reasoning 15 - Basic Deduction 16 - Basic Induction 17 - Positional Reasoning 18 - Size Reasoning 19 - Path Finding 20 - Agentâs Motivations Mean Performance M SV features +SRL 95 ⥠req. ex. of No. RY N O N M E Mem M E PTIV DA A 100 100 100 69 83 52 78 90 71 57 100 100 100 100 73 100 46 50 9 100 79 (2014) N N Mem al. et Weston 100 100 20 71 83 47 68 77 65 59 100 100 100 99 74 27 54 57 0 100 75 R A E N N NLIN O N + Structured N MS N A R N-G + N NL N + G N + N-gram Classiï¬er M LST Mem Mem Mem F E R O C M A 100 100 100 73 86 100 83 94 100 97 100 100 100 100 77 100 57 54 15 100 87",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 31 | Note that TRPO learned all of the gaits with general- purpose policies and simple reward functions, using min- imal prior knowledge. This is in contrast with most prior methods for learning locomotion, which typically rely on hand-architected policy classes that explicitly encode no- tions of balance and stepping (Tedrake et al., 2004; Geng et al., 2006; Wampler & Popovi´c, 2009).
# 8.2 Playing Games from Images
To evaluate TRPO on a partially observed task with com- plex observations, we trained policies for playing Atari games, using raw images as input. The games require learning a variety of behaviors, such as dodging bullets and hitting balls with paddles. Aside from the high dimension- ality, challenging elements of these games include delayed rewards (no immediate penalty is incurred when a life is lost in Breakout or Space Invaders); complex sequences of behavior (Q*bert requires a character to hop on 21 differ- ent platforms); and non-stationary image statistics (Enduro involves a changing and ï¬ickering background).
We tested our algorithms on the same seven games reported on in (Mnih et al., 2013) and (Guo et al., 2014), which are
Trust Region Policy Optimization | 1502.05477#31 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 31,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Note that TRPO learned all of the gaits with general- purpose policies and simple reward functions, using min- imal prior knowledge. This is in contrast with most prior methods for learning locomotion, which typically rely on hand-architected policy classes that explicitly encode no- tions of balance and stepping (Tedrake et al., 2004; Geng et al., 2006; Wampler & Popovi´c, 2009).\n# 8.2 Playing Games from Images\nTo evaluate TRPO on a partially observed task with com- plex observations, we trained policies for playing Atari games, using raw images as input. The games require learning a variety of behaviors, such as dodging bullets and hitting balls with paddles. Aside from the high dimension- ality, challenging elements of these games include delayed rewards (no immediate penalty is incurred when a life is lost in Breakout or Space Invaders); complex sequences of behavior (Q*bert requires a character to hop on 21 differ- ent platforms); and non-stationary image statistics (Enduro involves a changing and ï¬ickering background).\nWe tested our algorithms on the same seven games reported on in (Mnih et al., 2013) and (Guo et al., 2014), which are\nTrust Region Policy Optimization",
"title": "Trust Region Policy Optimization",
"year": 2015
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] |
1502.05698 | 31 | Classiï¬er M LST Mem Mem Mem F E R O C M A 100 100 100 73 86 100 83 94 100 97 100 100 100 100 77 100 57 54 15 100 87 M A 100 100 99 100 86 53 86 88 63 54 100 100 100 99 100 100 49 74 3 100 83 M A 100 100 100 100 98 100 85 91 100 98 100 100 100 99 100 100 65 95 36 100 93 99 74 17 98 83 99 69 70 100 99 100 96 99 99 96 24 61 62 49 95 79 36 2 7 50 20 49 52 40 62 45 29 9 26 19 20 43 46 52 0 76 34 250 ex. 500 ex. 500 ex. 500 ex. 1000 ex. 500 ex. FAIL FAIL 500 ex. 1000 ex. 250 ex. 250 ex. 250 ex. 500 ex. 100 ex. 100 ex. FAIL 1000 ex. FAIL 250 ex. 100 50 20 20 61 70 48 49 45 64 44 72 74 94 27 21 23 51 52 8 91 49 Training MultiTask 100 100 98 80 99 100 86 93 100 98 100 100 100 99 100 94 72 93 19 100 92 | 1502.05698#31 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 31,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Classiï¬er M LST Mem Mem Mem F E R O C M A 100 100 100 73 86 100 83 94 100 97 100 100 100 100 77 100 57 54 15 100 87 M A 100 100 99 100 86 53 86 88 63 54 100 100 100 99 100 100 49 74 3 100 83 M A 100 100 100 100 98 100 85 91 100 98 100 100 100 99 100 100 65 95 36 100 93 99 74 17 98 83 99 69 70 100 99 100 96 99 99 96 24 61 62 49 95 79 36 2 7 50 20 49 52 40 62 45 29 9 26 19 20 43 46 52 0 76 34 250 ex. 500 ex. 500 ex. 500 ex. 1000 ex. 500 ex. FAIL FAIL 500 ex. 1000 ex. 250 ex. 250 ex. 250 ex. 500 ex. 100 ex. 100 ex. FAIL 1000 ex. FAIL 250 ex. 100 50 20 20 61 70 48 49 45 64 44 72 74 94 27 21 23 51 52 8 91 49 Training MultiTask 100 100 98 80 99 100 86 93 100 98 100 100 100 99 100 94 72 93 19 100 92",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 32 | We tested our algorithms on the same seven games reported on in (Mnih et al., 2013) and (Guo et al., 2014), which are
Trust Region Policy Optimization
B. Rider Breakout Enduro Pong Q*bert Seaquest S. Invaders Random Human (Mnih et al., 2013) 354 7456 1.2 31.0 0 368 â20.4 â3.0 157 18900 110 28010 179 3690 Deep Q Learning (Mnih et al., 2013) 4092 168.0 470 20.0 1952 1705 581 UCC-I (Guo et al., 2014) 5702 380 741 21 20025 2995 692 TRPO - single path TRPO - vine 1425.2 859.5 10.8 34.2 534.6 430.8 20.9 20.9 1973.5 7732.5 1908.6 788.4 568.4 450.2
Table 1. Performance comparison for vision-based RL algorithms on the Atari domain. Our algorithms (bottom rows) were run once on each task, with the same architecture and parameters. Performance varies substantially from run to run (with different random initializations of the policy), but we could not obtain error statistics due to time constraints. | 1502.05477#32 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 32,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "We tested our algorithms on the same seven games reported on in (Mnih et al., 2013) and (Guo et al., 2014), which are\nTrust Region Policy Optimization\nB. Rider Breakout Enduro Pong Q*bert Seaquest S. Invaders Random Human (Mnih et al., 2013) 354 7456 1.2 31.0 0 368 â20.4 â3.0 157 18900 110 28010 179 3690 Deep Q Learning (Mnih et al., 2013) 4092 168.0 470 20.0 1952 1705 581 UCC-I (Guo et al., 2014) 5702 380 741 21 20025 2995 692 TRPO - single path TRPO - vine 1425.2 859.5 10.8 34.2 534.6 430.8 20.9 20.9 1973.5 7732.5 1908.6 788.4 568.4 450.2\nTable 1. Performance comparison for vision-based RL algorithms on the Atari domain. Our algorithms (bottom rows) were run once on each task, with the same architecture and parameters. Performance varies substantially from run to run (with different random initializations of the policy), but we could not obtain error statistics due to time constraints.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 32 | Methods The N -gram classiï¬er baseline is inspired by the baselines in Richardson et al. (2013) but applied to the case of producing a 1-word answer rather than a multiple choice question: we construct a bag-of-N -grams for all sentences in the story that share at least one word with the question, and then learn a linear classiï¬er to predict the answer using those features4.
LSTMs are a popular method for sequence prediction (Sutskever et al., 2014) and outperform stan- dard RNNs (Recurrent Neural Networks) for tasks similar to ours (Weston et al., 2014). They work by reading the story until the point they reach a question and then have to output an answer. Note that they are weakly supervised by answers only, and are hence at a disadvantage compared to strongly supervised methods or methods that use external resources. | 1502.05698#32 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 32,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Methods The N -gram classiï¬er baseline is inspired by the baselines in Richardson et al. (2013) but applied to the case of producing a 1-word answer rather than a multiple choice question: we construct a bag-of-N -grams for all sentences in the story that share at least one word with the question, and then learn a linear classiï¬er to predict the answer using those features4.\nLSTMs are a popular method for sequence prediction (Sutskever et al., 2014) and outperform stan- dard RNNs (Recurrent Neural Networks) for tasks similar to ours (Weston et al., 2014). They work by reading the story until the point they reach a question and then have to output an answer. Note that they are weakly supervised by answers only, and are hence at a disadvantage compared to strongly supervised methods or methods that use external resources.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 33 | made available through the Arcade Learning Environment (Bellemare et al., 2013) The images were preprocessed fol- lowing the protocol in Mnih et al (2013), and the policy was represented by the convolutional neural network shown in Figure 3, with two convolutional layers with 16 channels and stride 2, followed by one fully-connected layer with 20 units, yielding 33,500 parameters.
The results of the vine and single path algorithms are sum- marized in Table 1, which also includes an expert human performance and two recent methods: deep Q-learning (Mnih et al., 2013), and a combination of Monte-Carlo Tree Search with supervised training (Guo et al., 2014), called UCC-I. The 500 iterations of our algorithm took about 30 hours (with slight variation between games) on a 16-core computer. While our method only outperformed the prior methods on some of the games, it consistently achieved rea- sonable scores. Unlike the prior methods, our approach was not designed speciï¬cally for this task. The ability to apply the same policy search method to methods as di- verse as robotic locomotion and image-based game playing demonstrates the generality of TRPO. | 1502.05477#33 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 33,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "made available through the Arcade Learning Environment (Bellemare et al., 2013) The images were preprocessed fol- lowing the protocol in Mnih et al (2013), and the policy was represented by the convolutional neural network shown in Figure 3, with two convolutional layers with 16 channels and stride 2, followed by one fully-connected layer with 20 units, yielding 33,500 parameters.\nThe results of the vine and single path algorithms are sum- marized in Table 1, which also includes an expert human performance and two recent methods: deep Q-learning (Mnih et al., 2013), and a combination of Monte-Carlo Tree Search with supervised training (Guo et al., 2014), called UCC-I. The 500 iterations of our algorithm took about 30 hours (with slight variation between games) on a 16-core computer. While our method only outperformed the prior methods on some of the games, it consistently achieved rea- sonable scores. Unlike the prior methods, our approach was not designed speciï¬cally for this task. The ability to apply the same policy search method to methods as di- verse as robotic locomotion and image-based game playing demonstrates the generality of TRPO.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 33 | MemNNs (Weston et al., 2014) are a recently proposed class of models that have been shown to perform well at QA. They work by a âcontrollerâ neural network performing inference over the stored memories that consist of the previous statements in the story. The original proposed model performs 2 hops of inference: ï¬nding the ï¬rst supporting fact with the maximum match score with the question, and then the second supporting fact with the maximum match score with both the question and the ï¬rst fact that was found. The matching function consists of mapping the bag-of- words for the question and facts into an embedding space by summing word embeddings. The word embeddings are learnt using strong supervision to optimize the QA task. After ï¬nding supporting facts, a ï¬nal ranking is performed to rank possible responses (answer words) given those facts. We also consider some extensions to this model:
⢠Adaptive memories performing a variable number of hops rather than 2, the model is trained to predict a hop or the special âSTOPâ class. A similar procedure can be applied to output multiple tokens as well.
4Constructing N -grams from all sentences rather than using the ï¬ltered set gave worse results.
7
# Under review as a conference paper at ICLR 2016 | 1502.05698#33 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 33,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "MemNNs (Weston et al., 2014) are a recently proposed class of models that have been shown to perform well at QA. They work by a âcontrollerâ neural network performing inference over the stored memories that consist of the previous statements in the story. The original proposed model performs 2 hops of inference: ï¬nding the ï¬rst supporting fact with the maximum match score with the question, and then the second supporting fact with the maximum match score with both the question and the ï¬rst fact that was found. The matching function consists of mapping the bag-of- words for the question and facts into an embedding space by summing word embeddings. The word embeddings are learnt using strong supervision to optimize the QA task. After ï¬nding supporting facts, a ï¬nal ranking is performed to rank possible responses (answer words) given those facts. We also consider some extensions to this model:\n⢠Adaptive memories performing a variable number of hops rather than 2, the model is trained to predict a hop or the special âSTOPâ class. A similar procedure can be applied to output multiple tokens as well.\n4Constructing N -grams from all sentences rather than using the ï¬ltered set gave worse results.\n7\n# Under review as a conference paper at ICLR 2016",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 34 | the game-playing domain, we learned convolutional neu- ral network policies that used raw images as inputs. This requires optimizing extremely high-dimensional policies, and only two prior methods report successful results on this task.
Since the method we proposed is scalable and has strong theoretical foundations, we hope that it will serve as a jumping-off point for future work on training large, rich function approximators for a range of challenging prob- lems. At the intersection of the two experimental domains we explored, there is the possibility of learning robotic con- trol policies that use vision and raw sensory data as in- put, providing a uniï¬ed scheme for training robotic con- trollers that perform both perception and control. The use of more sophisticated policies, including recurrent policies with hidden state, could further make it possible to roll state estimation and control into the same policy in the partially- observed setting. By combining our method with model learning, it would also be possible to substantially reduce its sample complexity, making it applicable to real-world settings where samples are expensive.
# 9 Discussion
# Acknowledgements | 1502.05477#34 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 34,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "the game-playing domain, we learned convolutional neu- ral network policies that used raw images as inputs. This requires optimizing extremely high-dimensional policies, and only two prior methods report successful results on this task.\nSince the method we proposed is scalable and has strong theoretical foundations, we hope that it will serve as a jumping-off point for future work on training large, rich function approximators for a range of challenging prob- lems. At the intersection of the two experimental domains we explored, there is the possibility of learning robotic con- trol policies that use vision and raw sensory data as in- put, providing a uniï¬ed scheme for training robotic con- trollers that perform both perception and control. The use of more sophisticated policies, including recurrent policies with hidden state, could further make it possible to roll state estimation and control into the same policy in the partially- observed setting. By combining our method with model learning, it would also be possible to substantially reduce its sample complexity, making it applicable to real-world settings where samples are expensive.\n# 9 Discussion\n# Acknowledgements",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 34 | 4Constructing N -grams from all sentences rather than using the ï¬ltered set gave worse results.
7
# Under review as a conference paper at ICLR 2016
⢠N -grams We tried using a bag of 3-grams rather than a bag-of-words to represent the text. In both cases the ï¬rst step of the MemNN is to convert these into vectorial embeddings.
⢠Nonlinearity We apply a classical 2-layer neural network with tanh nonlinearity in the matching function.
More details of these variants is given in Sec A of the appendix. | 1502.05698#34 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 34,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "4Constructing N -grams from all sentences rather than using the ï¬ltered set gave worse results.\n7\n# Under review as a conference paper at ICLR 2016\n⢠N -grams We tried using a bag of 3-grams rather than a bag-of-words to represent the text. In both cases the ï¬rst step of the MemNN is to convert these into vectorial embeddings.\n⢠Nonlinearity We apply a classical 2-layer neural network with tanh nonlinearity in the matching function.\nMore details of these variants is given in Sec A of the appendix.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 35 | # 9 Discussion
# Acknowledgements
We proposed and analyzed trust region methods for opti- mizing stochastic control policies. We proved monotonic improvement for an algorithm that repeatedly optimizes a local approximation to the expected return of the pol- icy with a KL divergence penalty, and we showed that an approximation to this method that incorporates a KL di- vergence constraint achieves good empirical results on a range of challenging policy learning tasks, outperforming prior methods. Our analysis also provides a perspective that uniï¬es policy gradient and policy iteration methods, and shows them to be special limiting cases of an algo- rithm that optimizes a certain objective subject to a trust region constraint.
We thank Emo Todorov and Yuval Tassa for providing the MuJoCo simulator; Bruno Scherrer, Tom Erez, Greg Wayne, and the anonymous ICML reviewers for insightful comments, and Vitchyr Pong and Shane Gu for pointing our errors in a previous version of the manuscript. This re- search was funded in part by the Ofï¬ce of Naval Research through a Young Investigator Award and under grant num- ber N00014-11-1-0688, DARPA through a Young Faculty Award, by the Army Research Ofï¬ce through the MAST program.
# References | 1502.05477#35 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 35,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "# 9 Discussion\n# Acknowledgements\nWe proposed and analyzed trust region methods for opti- mizing stochastic control policies. We proved monotonic improvement for an algorithm that repeatedly optimizes a local approximation to the expected return of the pol- icy with a KL divergence penalty, and we showed that an approximation to this method that incorporates a KL di- vergence constraint achieves good empirical results on a range of challenging policy learning tasks, outperforming prior methods. Our analysis also provides a perspective that uniï¬es policy gradient and policy iteration methods, and shows them to be special limiting cases of an algo- rithm that optimizes a certain objective subject to a trust region constraint.\nWe thank Emo Todorov and Yuval Tassa for providing the MuJoCo simulator; Bruno Scherrer, Tom Erez, Greg Wayne, and the anonymous ICML reviewers for insightful comments, and Vitchyr Pong and Shane Gu for pointing our errors in a previous version of the manuscript. This re- search was funded in part by the Ofï¬ce of Naval Research through a Young Investigator Award and under grant num- ber N00014-11-1-0688, DARPA through a Young Faculty Award, by the Army Research Ofï¬ce through the MAST program.\n# References",
"title": "Trust Region Policy Optimization",
"year": 2015
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] |
1502.05698 | 35 | ⢠Nonlinearity We apply a classical 2-layer neural network with tanh nonlinearity in the matching function.
More details of these variants is given in Sec A of the appendix.
Finally, we built a classical cascade NLP system baseline using a structured support vector ma- chine (SVM), which incorporates coreference resolution and semantic role labeling (SRL) prepro- cessing steps, which are themselves trained on large amounts of costly labeled data. The Stanford coreference system (Raghunathan et al., 2010) and the SENNA semantic role labeling (SRL) system (Collobert et al., 2011) are used to build features for the input to the SVM, trained with strong super- vision to ï¬nd the supporting facts, e.g. features based on words, word pairs, and the SRL verb and verb-argument pairs. After ï¬nding the supporting facts, we build a similar structured SVM for the response stage, with features tuned for that goal as well. More details are in Sec. B of the appendix.
Learning rates and other hyperparameters for all methods are chosen using the training set. The summary of our experimental results on the tasks is given in Table 3. We give results for each of the 20 tasks separately, as well as mean performance and number of failed tasks in the ï¬nal two rows. | 1502.05698#35 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 35,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "⢠Nonlinearity We apply a classical 2-layer neural network with tanh nonlinearity in the matching function.\nMore details of these variants is given in Sec A of the appendix.\nFinally, we built a classical cascade NLP system baseline using a structured support vector ma- chine (SVM), which incorporates coreference resolution and semantic role labeling (SRL) prepro- cessing steps, which are themselves trained on large amounts of costly labeled data. The Stanford coreference system (Raghunathan et al., 2010) and the SENNA semantic role labeling (SRL) system (Collobert et al., 2011) are used to build features for the input to the SVM, trained with strong super- vision to ï¬nd the supporting facts, e.g. features based on words, word pairs, and the SRL verb and verb-argument pairs. After ï¬nding the supporting facts, we build a similar structured SVM for the response stage, with features tuned for that goal as well. More details are in Sec. B of the appendix.\nLearning rates and other hyperparameters for all methods are chosen using the training set. The summary of our experimental results on the tasks is given in Table 3. We give results for each of the 20 tasks separately, as well as mean performance and number of failed tasks in the ï¬nal two rows.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
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] |
1502.05477 | 36 | # References
In the domain of robotic locomotion, we successfully learned controllers for swimming, walking and hopping in a physics simulator, using general purpose neural networks and minimally informative rewards. To our knowledge, no prior work has learned controllers from scratch for all of these tasks, using a generic policy search method and non-engineered, general-purpose policy representations. In
Bagnell, J. A. and Schneider, J. Covariant policy search. IJCAI, 2003.
Bartlett, P. L. and Baxter, J. Inï¬nite-horizon policy-gradient esti- mation. arXiv preprint arXiv:1106.0665, 2011.
Barto, A., Sutton, R., and Anderson, C. Neuronlike adaptive ele- ments that can solve difï¬cult learning control problems. IEEE Transactions on Systems, Man and Cybernetics, (5):834â846, 1983.
Trust Region Policy Optimization
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M. The ar- cade learning environment: An evaluation platform for general agents. Journal of Artiï¬cial Intelligence Research, 47:253â 279, jun 2013. | 1502.05477#36 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 36,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "# References\nIn the domain of robotic locomotion, we successfully learned controllers for swimming, walking and hopping in a physics simulator, using general purpose neural networks and minimally informative rewards. To our knowledge, no prior work has learned controllers from scratch for all of these tasks, using a generic policy search method and non-engineered, general-purpose policy representations. In\nBagnell, J. A. and Schneider, J. Covariant policy search. IJCAI, 2003.\nBartlett, P. L. and Baxter, J. Inï¬nite-horizon policy-gradient esti- mation. arXiv preprint arXiv:1106.0665, 2011.\nBarto, A., Sutton, R., and Anderson, C. Neuronlike adaptive ele- ments that can solve difï¬cult learning control problems. IEEE Transactions on Systems, Man and Cybernetics, (5):834â846, 1983.\nTrust Region Policy Optimization\nBellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M. The ar- cade learning environment: An evaluation platform for general agents. Journal of Artiï¬cial Intelligence Research, 47:253â 279, jun 2013.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 36 | Results Standard MemNNs generally outperform the N -gram and LSTM baselines, which is con- sistent with the results in Weston et al. (2014). However they still âfailâ at a number of tasks; that is, test accuracy is less than 95%. Some of these failures are expected due to insufï¬cient modeling power as described in more detail in Sec. A.1, e.g. k = 2 facts, single word answers and bag-of- words do not succeed on tasks 3, 4, 5, 7, 8 and 18. However, there were also failures on tasks we did not at ï¬rst expect, for example yes/no questions (6) and indeï¬nite knowledge (10). Given hindsight, we realize that the linear scoring function of standard MemNNs cannot model the match between query, supporting fact and a yes/no answer as this requires three-way interactions. | 1502.05698#36 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 36,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Results Standard MemNNs generally outperform the N -gram and LSTM baselines, which is con- sistent with the results in Weston et al. (2014). However they still âfailâ at a number of tasks; that is, test accuracy is less than 95%. Some of these failures are expected due to insufï¬cient modeling power as described in more detail in Sec. A.1, e.g. k = 2 facts, single word answers and bag-of- words do not succeed on tasks 3, 4, 5, 7, 8 and 18. However, there were also failures on tasks we did not at ï¬rst expect, for example yes/no questions (6) and indeï¬nite knowledge (10). Given hindsight, we realize that the linear scoring function of standard MemNNs cannot model the match between query, supporting fact and a yes/no answer as this requires three-way interactions.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
1502.05477 | 37 | Peters, J. and Schaal, S. Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4):682â697, 2008a.
Bertsekas, D. Dynamic programming and optimal control, vol- ume 1. 2005.
Deisenroth, M., Neumann, G., and Peters, J. A survey on policy search for robotics. Foundations and Trends in Robotics, 2(1- 2):1â142, 2013.
Gabillon, Victor, Ghavamzadeh, Mohammad, and Scherrer, Bruno. Approximate dynamic programming ï¬nally performs well in the game of Tetris. In Advances in Neural Information Processing Systems, 2013.
Geng, T., Porr, B., and W¨org¨otter, F. Fast biped walking with a reï¬exive controller and realtime policy searching. In Advances in Neural Information Processing Systems (NIPS), 2006.
Guo, X., Singh, S., Lee, H., Lewis, R. L., and Wang, X. Deep learning for real-time atari game play using ofï¬ine Monte- Carlo tree search planning. In Advances in Neural Information Processing Systems, pp. 3338â3346, 2014. | 1502.05477#37 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 37,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Peters, J. and Schaal, S. Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4):682â697, 2008a.\nBertsekas, D. Dynamic programming and optimal control, vol- ume 1. 2005.\nDeisenroth, M., Neumann, G., and Peters, J. A survey on policy search for robotics. Foundations and Trends in Robotics, 2(1- 2):1â142, 2013.\nGabillon, Victor, Ghavamzadeh, Mohammad, and Scherrer, Bruno. Approximate dynamic programming ï¬nally performs well in the game of Tetris. In Advances in Neural Information Processing Systems, 2013.\nGeng, T., Porr, B., and W¨org¨otter, F. Fast biped walking with a reï¬exive controller and realtime policy searching. In Advances in Neural Information Processing Systems (NIPS), 2006.\nGuo, X., Singh, S., Lee, H., Lewis, R. L., and Wang, X. Deep learning for real-time atari game play using ofï¬ine Monte- Carlo tree search planning. In Advances in Neural Information Processing Systems, pp. 3338â3346, 2014.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 37 | Columns 5-9 of Table 3 give the results for our MemNN extensions: adaptive memories (AM), N -grams (NG) and nonlinearities (NL), plus combinations thereof. The adaptive approach gives a straight-forward improvement in tasks 3 and 16 because they both require more than two supporting facts, and also gives (small) improvements in 8 and 19 because they require multi-word outputs (but still remain difï¬cult). We hence use the AM model in combination with all our other extensions in the subsequent experiments.
MemNNs with N -gram modeling yield clear improvements when word order matters, e.g. tasks 4 and 15. However, N -grams do not seem to be a substitute for nonlinearities in the embedding function as the NL model outperforms N -grams on average, especially in the yes/no (6) and indef- inite tasks (10), as explained before. On the other hand, the NL method cannot model word order and so fails e.g., on task 4. The obvious step is thus to combine these complimentary approaches: indeed AM+NG+NL (column 9) gives improved results over both, with a total of 9 tasks that have been upgraded from failure to success compared to the original MemNN model. | 1502.05698#37 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 37,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "Columns 5-9 of Table 3 give the results for our MemNN extensions: adaptive memories (AM), N -grams (NG) and nonlinearities (NL), plus combinations thereof. The adaptive approach gives a straight-forward improvement in tasks 3 and 16 because they both require more than two supporting facts, and also gives (small) improvements in 8 and 19 because they require multi-word outputs (but still remain difï¬cult). We hence use the AM model in combination with all our other extensions in the subsequent experiments.\nMemNNs with N -gram modeling yield clear improvements when word order matters, e.g. tasks 4 and 15. However, N -grams do not seem to be a substitute for nonlinearities in the embedding function as the NL model outperforms N -grams on average, especially in the yes/no (6) and indef- inite tasks (10), as explained before. On the other hand, the NL method cannot model word order and so fails e.g., on task 4. The obvious step is thus to combine these complimentary approaches: indeed AM+NG+NL (column 9) gives improved results over both, with a total of 9 tasks that have been upgraded from failure to success compared to the original MemNN model.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 38 | Hansen, Nikolaus and Ostermeier, Andreas. Adapting arbitrary normal mutation distributions in evolution strategies: The co- In Evolutionary Computation, variance matrix adaptation. 1996., Proceedings of IEEE International Conference on, pp. 312â317. IEEE, 1996.
Peters, J., M¨ulling, K., and Alt¨un, Y. Relative entropy policy search. In AAAI Conference on Artiï¬cial Intelligence, 2010.
Peters, Jan and Schaal, Stefan. Natural actor-critic. Neurocom- puting, 71(7):1180â1190, 2008b.
Pirotta, Matteo, Restelli, Marcello, Pecorino, Alessio, and Calan- driello, Daniele. Safe policy iteration. In Proceedings of The 30th International Conference on Machine Learning, pp. 307â 315, 2013.
Pollard, David. Asymptopia: an exposition of statistical asymp- totic theory. 2000. URL http://www.stat.yale.edu/ Ëpollard/Books/Asymptopia.
Szita, Istv´an and L¨orincz, Andr´as. Learning tetris using the noisy cross-entropy method. Neural computation, 18(12): 2936â2941, 2006. | 1502.05477#38 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 38,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Hansen, Nikolaus and Ostermeier, Andreas. Adapting arbitrary normal mutation distributions in evolution strategies: The co- In Evolutionary Computation, variance matrix adaptation. 1996., Proceedings of IEEE International Conference on, pp. 312â317. IEEE, 1996.\nPeters, J., M¨ulling, K., and Alt¨un, Y. Relative entropy policy search. In AAAI Conference on Artiï¬cial Intelligence, 2010.\nPeters, Jan and Schaal, Stefan. Natural actor-critic. Neurocom- puting, 71(7):1180â1190, 2008b.\nPirotta, Matteo, Restelli, Marcello, Pecorino, Alessio, and Calan- driello, Daniele. Safe policy iteration. In Proceedings of The 30th International Conference on Machine Learning, pp. 307â 315, 2013.\nPollard, David. Asymptopia: an exposition of statistical asymp- totic theory. 2000. URL http://www.stat.yale.edu/ Ëpollard/Books/Asymptopia.\nSzita, Istv´an and L¨orincz, Andr´as. Learning tetris using the noisy cross-entropy method. Neural computation, 18(12): 2936â2941, 2006.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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1502.05698 | 38 | The structured SVM, despite having access to external resources, does not perform better, still fail- ing at 9 tasks. It does perform better than vanilla MemNNs (without extensions) on tasks 6, 9 and 10 where the hand-built feature conjunctions capture the necessary nonlinearities. However, com- pared to MemNN (AM+NG+NL) it seems to do signiï¬cantly worse on tasks requiring three (and sometimes, two) supporting facts (e.g. tasks 3, 16 and 2) presumably as ranking over so many possi- bilities introduces more mistakes. However, its non-greedy search does seem to help on other tasks, such as path ï¬nding (task 19) where search is very important. Since it relies on external resources speciï¬cally designed for English, it is unsure that it would perform as well on other languages, like Hindi, where such external resources might be of worse quality. | 1502.05698#38 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 38,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "The structured SVM, despite having access to external resources, does not perform better, still fail- ing at 9 tasks. It does perform better than vanilla MemNNs (without extensions) on tasks 6, 9 and 10 where the hand-built feature conjunctions capture the necessary nonlinearities. However, com- pared to MemNN (AM+NG+NL) it seems to do signiï¬cantly worse on tasks requiring three (and sometimes, two) supporting facts (e.g. tasks 3, 16 and 2) presumably as ranking over so many possi- bilities introduces more mistakes. However, its non-greedy search does seem to help on other tasks, such as path ï¬nding (task 19) where search is very important. Since it relies on external resources speciï¬cally designed for English, it is unsure that it would perform as well on other languages, like Hindi, where such external resources might be of worse quality.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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] |
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Kakade, Sham and Langford, John. Approximately optimal ap- In ICML, volume 2, pp. proximate reinforcement learning. 267â274, 2002. | 1502.05477#39 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 39,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Tedrake, R., Zhang, T., and Seung, H. Stochastic policy gradi- ent reinforcement learning on a simple 3d biped. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004.\nTodorov, Emanuel, Erez, Tom, and Tassa, Yuval. MuJoCo: A physics engine for model-based control. In Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pp. 5026â5033. IEEE, 2012.\nHunter, David R and Lange, Kenneth. A tutorial on MM algo- rithms. The American Statistician, 58(1):30â37, 2004.\nKakade, Sham. A natural policy gradient. In Advances in Neural Information Processing Systems, pp. 1057â1063. MIT Press, 2002.\nWampler, Kevin and Popovi´c, Zoran. Optimal gait and form for animal locomotion. In ACM Transactions on Graphics (TOG), volume 28, pp. 60. ACM, 2009.\nWright, Stephen J and Nocedal, Jorge. Numerical optimization, volume 2. Springer New York, 1999.\nKakade, Sham and Langford, John. Approximately optimal ap- In ICML, volume 2, pp. proximate reinforcement learning. 267â274, 2002.",
"title": "Trust Region Policy Optimization",
"year": 2015
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] |
1502.05698 | 39 | The ï¬nal two columns (10-11) give further analysis of the AM+NG+NL MemNN method. The second to last column (10) shows the minimum number of training examples required to achieve ⥠95% accuracy, or FAIL if this is not achieved with 1000 examples. This is important as it is not only desirable to perform well on a task, but also using the fewest number of examples (to generalize well, quickly). Most succeeding tasks require 100-500 examples. Task 8 requires 5000 examples and 7 requires 10000, hence they are labeled as FAIL. The latter task can presumably be solved by adding all the times an object is picked up, and subtracting the times it is dropped, which seems
8
# Under review as a conference paper at ICLR 2016
possible for an MemNN, but it does not do perfectly. Two tasks, positional reasoning 17 and path ï¬nding 19 cannot be solved even with 10000 examples, it seems those (and indeed more advanced forms of induction and deduction, which we plan to build) require a general search algorithm to be built into the inference procedure, which MemNN (and the other approaches tried) are lacking. | 1502.05698#39 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 39,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "The ï¬nal two columns (10-11) give further analysis of the AM+NG+NL MemNN method. The second to last column (10) shows the minimum number of training examples required to achieve ⥠95% accuracy, or FAIL if this is not achieved with 1000 examples. This is important as it is not only desirable to perform well on a task, but also using the fewest number of examples (to generalize well, quickly). Most succeeding tasks require 100-500 examples. Task 8 requires 5000 examples and 7 requires 10000, hence they are labeled as FAIL. The latter task can presumably be solved by adding all the times an object is picked up, and subtracting the times it is dropped, which seems\n8\n# Under review as a conference paper at ICLR 2016\npossible for an MemNN, but it does not do perfectly. Two tasks, positional reasoning 17 and path ï¬nding 19 cannot be solved even with 10000 examples, it seems those (and indeed more advanced forms of induction and deduction, which we plan to build) require a general search algorithm to be built into the inference procedure, which MemNN (and the other approaches tried) are lacking.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 40 | Kakade, Sham and Langford, John. Approximately optimal ap- In ICML, volume 2, pp. proximate reinforcement learning. 267â274, 2002.
Lagoudakis, Michail G and Parr, Ronald. Reinforcement learn- ing as classiï¬cation: Leveraging modern classiï¬ers. In ICML, volume 3, pp. 424â431, 2003.
Levin, D. A., Peres, Y., and Wilmer, E. L. Markov chains and mixing times. American Mathematical Society, 2009.
Levine, Sergey and Abbeel, Pieter. Learning neural network policies with guided policy search under unknown dynamics. In Advances in Neural Information Processing Systems, pp. 1071â1079, 2014.
Martens, J. and Sutskever, I. Training deep and recurrent networks with hessian-free optimization. In Neural Networks: Tricks of the Trade, pp. 479â535. Springer, 2012.
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
Nemirovski, Arkadi. Efï¬cient methods in convex programming. 2005. | 1502.05477#40 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 40,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Kakade, Sham and Langford, John. Approximately optimal ap- In ICML, volume 2, pp. proximate reinforcement learning. 267â274, 2002.\nLagoudakis, Michail G and Parr, Ronald. Reinforcement learn- ing as classiï¬cation: Leveraging modern classiï¬ers. In ICML, volume 3, pp. 424â431, 2003.\nLevin, D. A., Peres, Y., and Wilmer, E. L. Markov chains and mixing times. American Mathematical Society, 2009.\nLevine, Sergey and Abbeel, Pieter. Learning neural network policies with guided policy search under unknown dynamics. In Advances in Neural Information Processing Systems, pp. 1071â1079, 2014.\nMartens, J. and Sutskever, I. Training deep and recurrent networks with hessian-free optimization. In Neural Networks: Tricks of the Trade, pp. 479â535. Springer, 2012.\nMnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.\nNemirovski, Arkadi. Efï¬cient methods in convex programming. 2005.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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] |
1502.05698 | 40 | The last column shows the performance of AM+NG+NL MemNNs when training on all the tasks jointly, rather than just on a single one. The performance is generally encouragingly similar, showing such a model can learn many aspects of text understanding and reasoning simultaneously. The main issues are that these models still fail on several of the tasks, and use a far stronger form of supervision (using supporting facts) than is typically realistic.
# 6 DISCUSSION
A prerequisite set We developed a set of tasks that we believe are a prerequisite to full language understanding and reasoning. While any learner that can solve these tasks is not necessarily close to full reasoning, if a learner fails on any of our tasks then there are likely real-world tasks that it will fail on too (i.e., real-world tasks that require the same kind of reasoning). Even if the situations and the language of the tasks are artiï¬cial, we believe that the mechanisms required to learn how to solve them are part of the key towards text understanding and reasoning. | 1502.05698#40 | Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks | One long-term goal of machine learning research is to produce methods that
are applicable to reasoning and natural language, in particular building an
intelligent dialogue agent. To measure progress towards that goal, we argue for
the usefulness of a set of proxy tasks that evaluate reading comprehension via
question answering. Our tasks measure understanding in several ways: whether a
system is able to answer questions via chaining facts, simple induction,
deduction and many more. The tasks are designed to be prerequisites for any
system that aims to be capable of conversing with a human. We believe many
existing learning systems can currently not solve them, and hence our aim is to
classify these tasks into skill sets, so that researchers can identify (and
then rectify) the failings of their systems. We also extend and improve the
recently introduced Memory Networks model, and show it is able to solve some,
but not all, of the tasks. | http://arxiv.org/pdf/1502.05698 | Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov | cs.AI, cs.CL, stat.ML | null | null | cs.AI | 20150219 | 20151231 | [
{
"id": "1511.02301"
},
{
"id": "1508.05508"
},
{
"id": "1506.02075"
}
] | {
"authors": "Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M. Rush, Bart van Merriënboer, Armand Joulin, Tomas Mikolov",
"chunk_id": 40,
"doc_id": "1502.05698",
"primary_category": "cs.AI",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05698",
"summary": "One long-term goal of machine learning research is to produce methods that\nare applicable to reasoning and natural language, in particular building an\nintelligent dialogue agent. To measure progress towards that goal, we argue for\nthe usefulness of a set of proxy tasks that evaluate reading comprehension via\nquestion answering. Our tasks measure understanding in several ways: whether a\nsystem is able to answer questions via chaining facts, simple induction,\ndeduction and many more. The tasks are designed to be prerequisites for any\nsystem that aims to be capable of conversing with a human. We believe many\nexisting learning systems can currently not solve them, and hence our aim is to\nclassify these tasks into skill sets, so that researchers can identify (and\nthen rectify) the failings of their systems. We also extend and improve the\nrecently introduced Memory Networks model, and show it is able to solve some,\nbut not all, of the tasks.",
"text": "The last column shows the performance of AM+NG+NL MemNNs when training on all the tasks jointly, rather than just on a single one. The performance is generally encouragingly similar, showing such a model can learn many aspects of text understanding and reasoning simultaneously. The main issues are that these models still fail on several of the tasks, and use a far stronger form of supervision (using supporting facts) than is typically realistic.\n# 6 DISCUSSION\nA prerequisite set We developed a set of tasks that we believe are a prerequisite to full language understanding and reasoning. While any learner that can solve these tasks is not necessarily close to full reasoning, if a learner fails on any of our tasks then there are likely real-world tasks that it will fail on too (i.e., real-world tasks that require the same kind of reasoning). Even if the situations and the language of the tasks are artiï¬cial, we believe that the mechanisms required to learn how to solve them are part of the key towards text understanding and reasoning.",
"title": "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks",
"year": 2015
} | [
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1502.05477 | 41 | Nemirovski, Arkadi. Efï¬cient methods in convex programming. 2005.
Ng, A. Y. and Jordan, M. PEGASUS: A policy search method for large mdps and pomdps. In Uncertainty in artiï¬cial intelli- gence (UAI), 2000.
Owen, Art B. Monte Carlo theory, methods and examples. 2013.
Pascanu, Razvan and Bengio, Yoshua. Revisiting natural gradient for deep networks. arXiv preprint arXiv:1301.3584, 2013.
Trust Region Policy Optimization
# A Proof of Policy Improvement Bound
This proof (of Theorem[I) uses techniques from the proof of Theorem 4.1 in (Kakade & Langford] 2002), adapting them to the more general setting considered in this paper. An informal overview is as follows. Our proof relies on the notion of coupling, where we jointly define the policies 7 and 7â so that they choose the same action with high probability = (1â a). Surrogate loss L,(i) accounts for the the advantage of 7 the first time that it disagrees with 7, but not subsequent disagreements. Hence, the error in L, is due to two or more disagreements between 7 and 7, hence, we get an O(a?) correction term, where a is the probability of disagreement. | 1502.05477#41 | Trust Region Policy Optimization | We describe an iterative procedure for optimizing policies, with guaranteed
monotonic improvement. By making several approximations to the
theoretically-justified procedure, we develop a practical algorithm, called
Trust Region Policy Optimization (TRPO). This algorithm is similar to natural
policy gradient methods and is effective for optimizing large nonlinear
policies such as neural networks. Our experiments demonstrate its robust
performance on a wide variety of tasks: learning simulated robotic swimming,
hopping, and walking gaits; and playing Atari games using images of the screen
as input. Despite its approximations that deviate from the theory, TRPO tends
to give monotonic improvement, with little tuning of hyperparameters. | http://arxiv.org/pdf/1502.05477 | John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel | cs.LG | 16 pages, ICML 2015 | null | cs.LG | 20150219 | 20170420 | [] | {
"authors": "John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel",
"chunk_id": 41,
"doc_id": "1502.05477",
"primary_category": "cs.LG",
"published": 20150219,
"source": "http://arxiv.org/pdf/1502.05477",
"summary": "We describe an iterative procedure for optimizing policies, with guaranteed\nmonotonic improvement. By making several approximations to the\ntheoretically-justified procedure, we develop a practical algorithm, called\nTrust Region Policy Optimization (TRPO). This algorithm is similar to natural\npolicy gradient methods and is effective for optimizing large nonlinear\npolicies such as neural networks. Our experiments demonstrate its robust\nperformance on a wide variety of tasks: learning simulated robotic swimming,\nhopping, and walking gaits; and playing Atari games using images of the screen\nas input. Despite its approximations that deviate from the theory, TRPO tends\nto give monotonic improvement, with little tuning of hyperparameters.",
"text": "Nemirovski, Arkadi. Efï¬cient methods in convex programming. 2005.\nNg, A. Y. and Jordan, M. PEGASUS: A policy search method for large mdps and pomdps. In Uncertainty in artiï¬cial intelli- gence (UAI), 2000.\nOwen, Art B. Monte Carlo theory, methods and examples. 2013.\nPascanu, Razvan and Bengio, Yoshua. Revisiting natural gradient for deep networks. arXiv preprint arXiv:1301.3584, 2013.\nTrust Region Policy Optimization\n# A Proof of Policy Improvement Bound\nThis proof (of Theorem[I) uses techniques from the proof of Theorem 4.1 in (Kakade & Langford] 2002), adapting them to the more general setting considered in this paper. An informal overview is as follows. Our proof relies on the notion of coupling, where we jointly define the policies 7 and 7â so that they choose the same action with high probability = (1â a). Surrogate loss L,(i) accounts for the the advantage of 7 the first time that it disagrees with 7, but not subsequent disagreements. Hence, the error in L, is due to two or more disagreements between 7 and 7, hence, we get an O(a?) correction term, where a is the probability of disagreement.",
"title": "Trust Region Policy Optimization",
"year": 2015
} | [
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