TQC implementation for PandaPickAndPlace-v3
Browse files- README.md +37 -0
- config.json +1 -0
- replay.mp4 +0 -0
- results.json +1 -0
- tqc-PandaPickAndPlace-v3.zip +3 -0
- tqc-PandaPickAndPlace-v3/_stable_baselines3_version +1 -0
- tqc-PandaPickAndPlace-v3/actor.optimizer.pth +3 -0
- tqc-PandaPickAndPlace-v3/critic.optimizer.pth +3 -0
- tqc-PandaPickAndPlace-v3/data +122 -0
- tqc-PandaPickAndPlace-v3/ent_coef_optimizer.pth +3 -0
- tqc-PandaPickAndPlace-v3/policy.pth +3 -0
- tqc-PandaPickAndPlace-v3/pytorch_variables.pth +3 -0
- tqc-PandaPickAndPlace-v3/system_info.txt +9 -0
- vec_normalize.pkl +3 -0
README.md
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---
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library_name: stable-baselines3
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tags:
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- PandaPickAndPlace-v3
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- deep-reinforcement-learning
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- reinforcement-learning
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- stable-baselines3
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model-index:
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- name: TQC
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results:
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- task:
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type: reinforcement-learning
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name: reinforcement-learning
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dataset:
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name: PandaPickAndPlace-v3
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type: PandaPickAndPlace-v3
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metrics:
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- type: mean_reward
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value: -40.70 +/- 18.67
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name: mean_reward
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verified: false
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---
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# **TQC** Agent playing **PandaPickAndPlace-v3**
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This is a trained model of a **TQC** agent playing **PandaPickAndPlace-v3**
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using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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## Usage (with Stable-baselines3)
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TODO: Add your code
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```python
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from stable_baselines3 import ...
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from huggingface_sb3 import load_from_hub
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...
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```
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config.json
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{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "sb3_contrib.tqc.policies", "__doc__": "\n Policy class (with both actor and critic) for TQC.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", "__init__": "<function MultiInputPolicy.__init__ at 0x7fde2c613d00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fde2c616e00>"}, "verbose": 1, "policy_kwargs": {"net_arch": [512, 512, 512], "n_critics": 2, "n_quantiles": 25, "use_sde": false}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717692926491504264, "learning_rate": 0.001, "tensorboard_log": "./tqcPandaPickAndPlace-v3/", "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": 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| 1 |
+
{
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| 2 |
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"policy_class": {
|
| 3 |
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":type:": "<class 'abc.ABCMeta'>",
|
| 4 |
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":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
|
| 5 |
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"__module__": "sb3_contrib.tqc.policies",
|
| 6 |
+
"__doc__": "\n Policy class (with both actor and critic) for TQC.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
|
| 7 |
+
"__init__": "<function MultiInputPolicy.__init__ at 0x7fde2c613d00>",
|
| 8 |
+
"__abstractmethods__": "frozenset()",
|
| 9 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fde2c616e00>"
|
| 10 |
+
},
|
| 11 |
+
"verbose": 1,
|
| 12 |
+
"policy_kwargs": {
|
| 13 |
+
"net_arch": [
|
| 14 |
+
512,
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| 15 |
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512,
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| 16 |
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512
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| 17 |
+
],
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| 18 |
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"n_critics": 2,
|
| 19 |
+
"n_quantiles": 25,
|
| 20 |
+
"use_sde": false
|
| 21 |
+
},
|
| 22 |
+
"num_timesteps": 1000000,
|
| 23 |
+
"_total_timesteps": 1000000,
|
| 24 |
+
"_num_timesteps_at_start": 0,
|
| 25 |
+
"seed": null,
|
| 26 |
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"action_noise": null,
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| 27 |
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"start_time": 1717692926491504264,
|
| 28 |
+
"learning_rate": 0.001,
|
| 29 |
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"tensorboard_log": "./tqcPandaPickAndPlace-v3/",
|
| 30 |
+
"_last_obs": {
|
| 31 |
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":type:": "<class 'collections.OrderedDict'>",
|
| 32 |
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":serialized:": "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",
|
| 33 |
+
"achieved_goal": "[[ 0.5656379 -2.5006437 0.13721822]\n [ 1.0558828 0.49241674 0.13720654]\n [ 0.93500805 -0.8696595 0.13719714]\n [ 1.1111599 -0.61482334 0.13720657]\n [ 0.80181324 0.6719262 0.1372141 ]\n [-0.9059582 -0.24872382 0.13721715]\n [-0.11599736 0.93694025 0.13721667]\n [ 0.67381877 0.1560354 0.13721274]\n [-1.0551797 1.0952677 0.13721782]\n [ 0.13098355 0.5550405 0.17496751]\n [ 1.3981552 0.69610035 0.13721249]\n [ 0.81952894 0.13264169 0.1374103 ]\n [ 0.64349985 -0.14730814 0.1374103 ]\n [ 0.4520654 0.6644759 0.13721417]\n [ 0.76782864 -0.05079028 0.24327667]\n [-0.7351678 -0.17136869 0.13720654]]",
|
| 34 |
+
"desired_goal": "[[-1.5941948 -0.967997 0.51534826]\n [ 1.4868279 0.03774566 0.5575534 ]\n [-0.83693707 1.6008574 -0.06440406]\n [-1.5604595 -0.9299377 0.26154956]\n [-1.3528793 -1.4772614 -0.20965055]\n [-1.7188047 -0.5559962 0.29591691]\n [-1.5820968 -0.13077642 -1.1271907 ]\n [ 0.76178277 -1.4011796 -0.6623946 ]\n [-0.04294314 0.6633103 -1.1271907 ]\n [-0.4863787 1.4754773 -1.1271907 ]\n [ 0.8360694 -1.2334995 1.2090732 ]\n [ 0.7942103 0.9294816 1.4300641 ]\n [ 1.3049996 1.1934425 1.0470237 ]\n [-0.02676739 -0.529748 -1.0942208 ]\n [ 1.3442129 0.1060029 0.3517552 ]\n [ 0.4388289 -1.1573216 -0.43994075]]",
|
| 35 |
+
"observation": "[[ 6.98849186e-02 -5.92110902e-02 -7.67757714e-01 6.92239165e-01\n 8.47134709e-01 -2.45346185e-02 8.77118707e-01 5.65637887e-01\n -2.50064373e+00 1.37218222e-01 -1.25593841e-02 -2.13627089e-02\n 5.35350204e-01 6.35737777e-01 -2.54065728e+00 6.36286363e-02\n 4.37840214e-03 -2.52415631e-02 8.79333496e-01]\n [ 1.95906967e-01 1.64755082e+00 -7.86407053e-01 7.27054775e-01\n 3.06956828e-01 -1.04404449e+00 1.14278936e+00 1.05588281e+00\n 4.92416739e-01 1.37206540e-01 -1.25617152e-02 -2.11469717e-02\n -1.04802484e-02 4.98090349e-02 -2.15986632e-02 6.32748306e-02\n 4.93753003e-03 -2.27960646e-02 -5.32469619e-03]\n [-2.53261089e-01 1.13713764e-01 -2.65132040e-01 8.50819170e-01\n -2.88771540e-01 -1.25380177e-02 1.09883714e+00 9.35008049e-01\n -8.69659483e-01 1.37197137e-01 -1.23275779e-02 -2.12958176e-02\n -1.03816586e-02 4.96315435e-02 -2.15275567e-02 6.32748306e-02\n 4.93753143e-03 -2.27960646e-02 -5.33550326e-03]\n [ 7.55780756e-01 6.19439781e-01 -7.60757804e-01 1.72594190e-01\n 9.38645780e-01 1.01650372e-01 -8.56046677e-01 1.11115992e+00\n -6.14823341e-01 1.37206569e-01 -1.25617152e-02 -2.11474039e-02\n -1.04407305e-02 4.98096235e-02 -2.15986650e-02 6.32742271e-02\n 4.93752956e-03 -2.27924269e-02 -5.32469060e-03]\n [ 1.04863882e+00 -6.82448924e-01 -7.45458663e-01 8.78427684e-01\n 1.60333073e+00 3.64960283e-02 -1.06972170e+00 8.01813245e-01\n 6.71926200e-01 1.37214094e-01 -1.25715071e-02 -2.14691740e-02\n -1.07180905e-02 4.95603420e-02 -2.13567130e-02 6.34496585e-02\n 3.70132341e-03 -2.47745346e-02 -5.41841378e-03]\n [ 9.06550735e-02 -4.37933654e-01 -7.56876230e-01 -4.81793702e-01\n -8.57221961e-01 5.82011752e-02 1.14839804e+00 -9.05958176e-01\n -2.48723820e-01 1.37217149e-01 -1.25501296e-02 -2.13409495e-02\n -9.80795361e-03 4.97968979e-02 -2.15147082e-02 6.32885098e-02\n 4.97639040e-03 -2.28392798e-02 -5.27913263e-03]\n [ 6.39747739e-01 1.07641876e+00 -4.64466929e-01 3.45315158e-01\n 7.30461860e-03 7.91939378e-01 1.14845157e+00 -1.15997359e-01\n 9.36940253e-01 1.37216672e-01 -1.25470487e-02 -2.13625077e-02\n -1.44700634e+00 4.98163514e-02 -2.15266831e-02 6.32978082e-02\n 5.04928129e-03 -2.27160454e-02 -5.27978549e-03]\n [ 1.04179472e-01 2.40135717e+00 6.09825850e-01 -2.59489124e-03\n 3.85957927e-01 1.53130913e+00 -1.06986010e+00 6.73818767e-01\n 1.56035393e-01 1.37212738e-01 -1.25292558e-02 -2.12326944e-02\n -3.85719872e+00 5.01983091e-02 -2.14953199e-02 6.31049499e-02\n 4.89169452e-03 -2.04758365e-02 -5.22998348e-03]\n [-2.93842733e-01 7.64783740e-01 -5.59514523e-01 4.97430772e-01\n 2.34728843e-01 3.16475600e-01 -1.06986511e+00 -1.05517972e+00\n 1.09526765e+00 1.37217820e-01 -1.25469053e-02 -2.13459879e-02\n -1.02300746e-02 4.98033650e-02 -2.15087160e-02 6.32752180e-02\n 4.93556121e-03 -2.27964353e-02 -5.27978549e-03]\n [ 4.32374775e-01 1.19953081e-01 -7.49285400e-01 -6.97919965e-01\n 1.55773759e+00 8.88358336e-03 1.14698517e+00 1.30983546e-01\n 5.55040479e-01 1.74967512e-01 -3.43154252e-01 -3.30453292e-02\n -2.54399300e-01 -3.83206701e+00 1.02196770e+01 -6.39237046e-01\n 3.18730593e+00 1.04158008e+00 -1.96637654e+00]\n [ 9.65550959e-01 -2.89079428e-01 -7.58524299e-01 3.24858844e-01\n -1.29193568e+00 3.79206426e-02 -1.06929171e+00 1.39815521e+00\n 6.96100354e-01 1.37212485e-01 -1.25656147e-02 -2.12461464e-02\n 5.57152867e-01 4.98929583e-02 -2.14107670e-02 6.32748082e-02\n 4.93748114e-03 -2.27959696e-02 -5.51934820e-03]\n [ 5.94249129e-01 1.80839092e-01 5.23474097e-01 2.89878517e-01\n 5.86964116e-02 5.67936003e-02 -1.06987059e+00 8.19528937e-01\n 1.32641688e-01 1.37410298e-01 -1.25470487e-02 -2.13460326e-02\n -1.03127398e-02 4.98034284e-02 -2.15090271e-02 6.32748306e-02\n 4.93753003e-03 -2.27960646e-02 -5.27978549e-03]\n [ 5.94249129e-01 1.80839092e-01 5.23474097e-01 2.89878517e-01\n 5.86964116e-02 5.67936003e-02 -1.06987059e+00 6.43499851e-01\n -1.47308141e-01 1.37410298e-01 -1.25470487e-02 -2.13460326e-02\n -1.03127398e-02 4.98034284e-02 -2.15090271e-02 6.32748306e-02\n 4.93753003e-03 -2.27960646e-02 -5.27978549e-03]\n [ 6.99885249e-01 9.08443153e-01 -3.25836927e-01 2.55256087e-01\n -1.06759751e+00 4.17296916e-01 1.14845181e+00 4.52065408e-01\n 6.64475918e-01 1.37214169e-01 -1.25610689e-02 -2.13175565e-02\n -9.90281440e-03 4.97646704e-02 -2.15395689e-02 6.33470193e-02\n 5.13083581e-03 -2.30360311e-02 -5.27978502e-03]\n [ 7.28081822e-01 2.63736486e-01 -4.33546335e-01 1.21943831e+00\n -5.91712475e-01 1.08367169e+00 -9.87249911e-01 7.67828643e-01\n -5.07902801e-02 2.43276671e-01 -1.30963940e-02 -1.85929549e+00\n -9.92574930e-01 7.78946340e-01 -4.12067413e-01 -7.57114232e-01\n 4.92216730e+00 6.58167124e+00 -3.51370692e+00]\n [ 8.02632701e-03 1.32118440e+00 -7.03490019e-01 3.17820460e-01\n 8.96896183e-01 4.90668744e-01 -1.03879499e+00 -7.35167801e-01\n -1.71368688e-01 1.37206540e-01 -1.25617152e-02 -2.11469736e-02\n -1.04338061e-02 4.98090349e-02 -2.15986650e-02 6.32748306e-02\n 4.93753003e-03 -2.27960646e-02 -5.32468874e-03]]"
|
| 36 |
+
},
|
| 37 |
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"_last_episode_starts": {
|
| 38 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 39 |
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":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAEBAQEBAQEBAQEBAQEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
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| 40 |
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},
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| 41 |
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"_last_original_obs": {
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| 42 |
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":type:": "<class 'abc.ABCMeta'>",
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"__doc__": "\n Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n Extends the ReplayBuffer to use dictionary observations\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
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|
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|
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|
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|
| 81 |
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},
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"_shape": null,
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"dtype": "float32",
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"bounded_below": "[ True True True True]",
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| 105 |
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"bounded_above": "[ True True True True]",
|
| 106 |
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"_shape": [
|
| 107 |
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4
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| 108 |
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],
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| 109 |
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"low": "[-1. -1. -1. -1.]",
|
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"high": "[1. 1. 1. 1.]",
|
| 111 |
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"low_repr": "-1.0",
|
| 112 |
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"high_repr": "1.0",
|
| 113 |
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"_np_random": "Generator(PCG64)"
|
| 114 |
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},
|
| 115 |
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"n_envs": 16,
|
| 116 |
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"lr_schedule": {
|
| 117 |
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":type:": "<class 'function'>",
|
| 118 |
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":serialized:": "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"
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| 119 |
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},
|
| 120 |
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"batch_norm_stats": [],
|
| 121 |
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"batch_norm_stats_target": []
|
| 122 |
+
}
|
tqc-PandaPickAndPlace-v3/ent_coef_optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
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|
|
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|
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|
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|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:acdfb7e234a1b7d10930e0fc07f46a990ad64fb1bec5fa059aeff901021e4c4b
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| 3 |
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size 1940
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tqc-PandaPickAndPlace-v3/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
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|
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|
|
| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:b69946b1ce5abdf52358fc904be79a91223736a5534536b0a6eaafe67f22f616
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size 11042200
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tqc-PandaPickAndPlace-v3/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
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|
|
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| 1 |
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version https://git-lfs.github.com/spec/v1
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size 1180
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tqc-PandaPickAndPlace-v3/system_info.txt
ADDED
|
@@ -0,0 +1,9 @@
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|
|
|
|
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|
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|
|
|
|
|
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|
| 1 |
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- OS: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Thu Jan 11 04:09:03 UTC 2024
|
| 2 |
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- Python: 3.10.12
|
| 3 |
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- Stable-Baselines3: 2.3.2
|
| 4 |
+
- PyTorch: 2.3.0+cu121
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
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- Numpy: 1.26.4
|
| 7 |
+
- Cloudpickle: 3.0.0
|
| 8 |
+
- Gymnasium: 0.29.1
|
| 9 |
+
- OpenAI Gym: 0.26.2
|
vec_normalize.pkl
ADDED
|
@@ -0,0 +1,3 @@
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|
|
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version https://git-lfs.github.com/spec/v1
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