Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 2153.28 +/- 57.83
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
+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-AntBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f89ae7e0989f8e97034d4793d4fefbf3d4965f831b3a0508d6ee539555326919
+size 129334

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0a9

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,106 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7f5d12d7f040>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5d12d7f0d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5d12d7f160>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5d12d7f1f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5d12d7f280>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5d12d7f310>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5d12d7f3a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5d12d7f430>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5d12d7f4c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5d12d7f550>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5d12d7f5e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5d12d7f670>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f5d12d81100>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            28
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678967547918835822,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAPLFAj9nXgc/vArGu1Gt4D4QcUw/PwJOPz65nT4RdJ48Sm6mv0iK6z/9qbY/SdocPbn72L7G/uM/rqDMv3kypr442Cs+gnC7vqkFDj9Wmv68xMNgP7ilY79uwh0/z4T8PscDOr/eOjY/s9NOwG5cKz9CQnQ+v1sZP9xBsr0zXx0/Ut6qP7nHlj9/Q8A+wVW4vrSKHb8qQ6Y+QQ6GPxcJAb8hQci+P644P/7vR78q576+da07P0RHYL/dDgo/qf7EPP5ggz8a2py/Cs9VPxW1vT7HAzq/3jo2P5Runj5uXCs/We2hvMZOzz5TB/09e4rLPhULoj5UO03AZxCHvjJeJT77Fjq/cC2HvqFlJT9sBQtAlB66vtRC97/eofQ9XDWjPgbMBz8F9pw/X8qZvgOfZL9r1tM//M8yQGwZH751O8q/xwM6vxfRs7+Ubp4+1Di/v6j/Aj9Nfwk/UBV7vK1YUD8A9LI/06acP+GAGT+kmsS+WzagvPocYz+4JqQ/ULeNvhvM+D6q4FI/Cug6v5WsNj5O2Tg/NvE5vwzdCT8uAaY8dv4zP5WIqr/X22c/4GBWPscDOr/eOjY/lG6ePm5cKz+UjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_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": 62500,
+    "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-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad03f838f811faa4cbf86e45a32f4798b1e7ba62bc7cedae1994fcfd4b62f827
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d46b49625d7ed9426043fd04a27b5567e726d9cc7961a42b5a9f5f7edabb545e
+size 56894

a2c-AntBulletEnv-v0/pytorch_variables.pth

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

a2c-AntBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-4.15.0-201-generic-x86_64-with-glibc2.27 # 212-Ubuntu SMP Mon Nov 28 11:29:59 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.8.0a9
+- PyTorch: 2.0.0+cu117
+- GPU Enabled: True
+- Numpy: 1.21.2
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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\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: Features extractor to use.\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 ActorCriticPolicy.__init__ at 0x7f5d12d7f040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5d12d7f0d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5d12d7f160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5d12d7f1f0>", "_build": "<function ActorCriticPolicy._build at 0x7f5d12d7f280>", "forward": "<function ActorCriticPolicy.forward at 0x7f5d12d7f310>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5d12d7f3a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5d12d7f430>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5d12d7f4c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5d12d7f550>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5d12d7f5e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5d12d7f670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f5d12d81100>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678967547918835822, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_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": 62500, "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-4.15.0-201-generic-x86_64-with-glibc2.27 # 212-Ubuntu SMP Mon Nov 28 11:29:59 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0a9", "PyTorch": "2.0.0+cu117", "GPU Enabled": "True", "Numpy": "1.21.2", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0a6043b9b2ca3c863183dc9ce68ae0dc47cecd3570c4104a666fb15fc5d2960
+size 1299883

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2153.281898355647, "std_reward": 57.831273681069945, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-16T14:19:27.594079"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e34d09e47d2546ddd44fab45c44a9069a0a72eb8190035d4704e2c99bf74ce8
+size 2170