Initial commit

.gitattributes

@@ -33,3 +33,4 @@ 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: 1513.13 +/- 249.07
+      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 sha256:8d794d75c89d4f6da2da7938aaf5cb26127d6b026edbe7ae48b550b8de72f923
+size 129248

a2c-AntBulletEnv-v0/_stable_baselines3_version

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

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 0x7f4f718d5510>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f718d55a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f718d5630>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4f718d56c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4f718d5750>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4f718d57e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4f718d5870>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4f718d5900>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4f718d5990>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4f718d5a20>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4f718d5ab0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4f718d5b40>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f4f718d8bc0>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1689080569476011023,
+    "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:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAABdBCU2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAij3HvQAAAACOJOC/AAAAAMaRAb0AAAAASPUAQAAAAAD3mHA9AAAAACiBAEAAAAAAcQqzPAAAAABEJO6/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAkfmONgAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgNry8L0AAAAAUU3cvwAAAADyLP88AAAAAKPQ7j8AAAAAIQI4PAAAAAARANs/AAAAAF1Um7wAAAAAJ3DkvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKc87QAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAICpXCg9AAAAAMTS7L8AAAAAFwocPQAAAACcpP0/AAAAAH3NlzwAAAAArwDtPwAAAAAUMRI9AAAAAGau/L8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADB0gY2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAKDuMPQAAAABNdOK/AAAAAP5Pd70AAAAAJkPnPwAAAACoiAM+AAAAAFY57D8AAAAAVOG/vQAAAADRBQDAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "_stats_window_size": 100,
+    "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,
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVbQIAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLHIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWcAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lGgLSxyFlIwBQ5R0lFKUjARoaWdolGgTKJZwAAAAAAAAAAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UaAtLHIWUaBZ0lFKUjA1ib3VuZGVkX2JlbG93lGgTKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLHIWUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUaCJLHIWUaBZ0lFKUjApfbnBfcmFuZG9tlE51Yi4=",
+        "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:256a976911e4a6120110f4750b3cf33c0e8d1b5944f7fd311dd00e7ca52c714d
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c47828694440fb2b10e7e5847c145d7eee9e07bfb9047c062191c4ec8b938e5d
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- 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 0x7f4f718d5510>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4f718d55a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4f718d5630>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4f718d56c0>", "_build": "<function ActorCriticPolicy._build at 0x7f4f718d5750>", "forward": "<function ActorCriticPolicy.forward at 0x7f4f718d57e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4f718d5870>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4f718d5900>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4f718d5990>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4f718d5a20>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4f718d5ab0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4f718d5b40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f4f718d8bc0>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689080569476011023, "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, "_stats_window_size": 100, "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, "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, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b87f3fda6be4c4befcc317e45210fbd5c2cd19008b9ef1026952749081021278
+size 1062613

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1513.131081325456, "std_reward": 249.07052323094285, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-11T14:05:09.549088"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cbc8e9747743f10e401e3e7384ad8a2c02e3d7aa433633f8c19d685fadaa62a
+size 2176