Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -1.78 +/- 0.15
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18bdfbe61d16fd32255ef1c336612c7fffb8dba936ff2266ddfa328a2a54a7de
+size 109436

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f042b4f50d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f042b5724c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.dict.Dict'>",
+        ":serialized:": "gAWVUgMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUaBCTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZSMAUOUdJRSlIwEaGlnaJRoHSiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBVLA4WUaCB0lFKUjA1ib3VuZGVkX2JlbG93lGgdKJYDAAAAAAAAAAEBAZRoEowCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIHSUUpSMDWJvdW5kZWRfYWJvdmWUaB0olgMAAAAAAAAAAQEBlGgsSwOFlGggdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBVoGEsDhZRoGmgdKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZRoIHSUUpRoI2gdKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFUsDhZRoIHSUUpRoKGgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoMmgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoN051YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgVaBhLBoWUaBpoHSiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBVLBoWUaCB0lFKUaCNoHSiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBVLBoWUaCB0lFKUaChoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDJoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDdOdWJ1aBhOaBBOaDdOdWIu",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678971805413123609,
+    "learning_rate": 0.00099,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.34933576 -0.01284632  0.5449023 ]\n [ 0.34933576 -0.01284632  0.5449023 ]\n [ 0.34933576 -0.01284632  0.5449023 ]\n [ 0.34933576 -0.01284632  0.5449023 ]]",
+        "desired_goal": "[[ 0.90514743  0.32704976 -1.2395029 ]\n [-1.0790703   0.9514146   0.8130439 ]\n [-0.8488252   0.03356663 -0.33864877]\n [ 0.04336137 -0.8347928   0.511936  ]]",
+        "observation": "[[ 0.34933576 -0.01284632  0.5449023  -0.00872186 -0.00159479 -0.00096527]\n [ 0.34933576 -0.01284632  0.5449023  -0.00872186 -0.00159479 -0.00096527]\n [ 0.34933576 -0.01284632  0.5449023  -0.00872186 -0.00159479 -0.00096527]\n [ 0.34933576 -0.01284632  0.5449023  -0.00872186 -0.00159479 -0.00096527]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[-0.02048577  0.004713    0.04602898]\n [-0.01577192 -0.07553396  0.1435948 ]\n [ 0.13318837  0.06030883  0.08670843]\n [ 0.00966567  0.08582342  0.22311106]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 31250,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5bcee0c441e9c699f0f2833e5635afe656f7edf37413b4fc7e8825ac095c570b
+size 45438

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:40f84ce9a7e5c4f41f3f3b69fbe1005bb0519acdf6abe889a0870936c06a6fd9
+size 46718

a2c-PandaReachDense-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f042b4f50d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f042b5724c0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678971805413123609, "learning_rate": 0.00099, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.34933576 -0.01284632  0.5449023 ]\n [ 0.34933576 -0.01284632  0.5449023 ]\n [ 0.34933576 -0.01284632  0.5449023 ]\n [ 0.34933576 -0.01284632  0.5449023 ]]", "desired_goal": "[[ 0.90514743  0.32704976 -1.2395029 ]\n [-1.0790703   0.9514146   0.8130439 ]\n [-0.8488252   0.03356663 -0.33864877]\n [ 0.04336137 -0.8347928   0.511936  ]]", "observation": "[[ 0.34933576 -0.01284632  0.5449023  -0.00872186 -0.00159479 -0.00096527]\n [ 0.34933576 -0.01284632  0.5449023  -0.00872186 -0.00159479 -0.00096527]\n [ 0.34933576 -0.01284632  0.5449023  -0.00872186 -0.00159479 -0.00096527]\n [ 0.34933576 -0.01284632  0.5449023  -0.00872186 -0.00159479 -0.00096527]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[-0.02048577  0.004713    0.04602898]\n [-0.01577192 -0.07553396  0.1435948 ]\n [ 0.13318837  0.06030883  0.08670843]\n [ 0.00966567  0.08582342  0.22311106]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 31250, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -1.783338677638676, "std_reward": 0.1487358164453886, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-16T13:49:20.604626"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ae5c5c95f78c70d1fe725885b75ecd10b5f50093598f596dbe06aa31b552132
+size 3056