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: 1197.97 +/- 173.30
+      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:c0162239236dcab64277c483fa2685a77493f2bb4da8e257f8406e4e11bd444d
+size 129265

a2c-AntBulletEnv-v0/_stable_baselines3_version

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

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 0x7f26b5af6ee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f26b5af6f70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f26b5afa040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f26b5afa0d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f26b5afa160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f26b5afa1f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f26b5afa280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f26b5afa310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f26b5afa3a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f26b5afa430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f26b5afa4c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f26b5afa550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f26b5af7ec0>"
+    },
+    "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": 1678985053265454404,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAK6lbj9YKYG+rsDgPjEecLys+ty/UqJewFxikD+8OZg/RHSDP/cpRD7q14Y/5sNsPsb91D/b+pK/P/qXv/+pbj8JhMm/s8m3PEcp9L9Q/Ai/HFxiv0xViz4b7hK/aoAIwEmFBT9Ii0DArnUFP7/VmL/VdoU/TEGVvoeD0z4h45E/Je4gvyWMlL74rMw+o8hIv4STkz8RJL09MRrcP78ZTz3Lc229cNk9v5xbI758cL2+lZyMvnBGVb/MoZ8+gavrPqwQLL8XfFM+6MA5v8J84TxJhQU/NS+qPq51BT+/1Zi/P1fXvh2DkD4Dnyw/pUgkv5DzIMAHJu2/qqAKPTY7Fb5PbCi+Xp2lv4veMb54ve6/u6P9v4WFjL9hCIc+Imwnvjitt787vUS/1nYGPzBAa79EHyO+LYCSvY23AL+VWrA/SYUFPzUvqj6udQU/v9WYv01AxD6otja/ch0cPaq1iTzdTyk9jeIHvykR3Dycy3+/GocvP6C+u7+2tDI/uCFbv2StAT/2c9i5SI5DP1d+XT9k85e/StGGPSw2ZD94+9g8pYvPPixtRD3Ex7o/ul6fPkmFBT81L6o+rnUFP7dmVj+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:e5d29c11c1b682dd315bef9f8afffbf17bf59c2a1488c5dfb32ad164e4729357
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8c5d4d531ee03fc242168fecb29ea6b40b0adb4ca001c5412c8c92949825647
+size 56958

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-5.10.147+-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:": "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 0x7f26b5af6ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f26b5af6f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f26b5afa040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f26b5afa0d0>", "_build": "<function ActorCriticPolicy._build at 0x7f26b5afa160>", "forward": "<function ActorCriticPolicy.forward at 0x7f26b5afa1f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f26b5afa280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f26b5afa310>", "_predict": "<function ActorCriticPolicy._predict at 0x7f26b5afa3a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f26b5afa430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f26b5afa4c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f26b5afa550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f26b5af7ec0>"}, "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:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAQEBAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAEBAQEBAQEBlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 1678985053265454404, "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-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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80fe09a40331f2584682e3910df27f63403d4d2866fdd5046eea11117410d6e1
+size 1019244

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1197.965620642039, "std_reward": 173.2974316552095, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-16T17:50:51.841062"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c640de3b016d610d7775fd520f45888bbc8088c5ab9111ae41fc55da40988c77
+size 2136