Initial commit

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: 1085.43 +/- 258.72
+      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 sha256:9e1080ca84f6c3f634d086beea73e082f3bcec822a62e2ae6e5661de92e352a4
+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 0x7f3d404c68b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3d404c6940>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3d404c69d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3d404c6a60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3d404c6af0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3d404c6b80>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3d404c6c10>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3d404c6ca0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3d404c6d30>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3d404c6dc0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3d404c6e50>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3d404c6ee0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f3d404c7880>"
+    },
+    "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": 1679235249689096427,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c79f3e37bc9af59b58bdfb9c9cf6a0c210fcd642c1b6bff823ab0999df1cc3cc
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85ee3f1b9638053c839f64abfd37517da77f169ab64c0333427296f975d4810c
+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 0x7f3d404c68b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3d404c6940>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3d404c69d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3d404c6a60>", "_build": "<function ActorCriticPolicy._build at 0x7f3d404c6af0>", "forward": "<function ActorCriticPolicy.forward at 0x7f3d404c6b80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3d404c6c10>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3d404c6ca0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3d404c6d30>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3d404c6dc0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3d404c6e50>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3d404c6ee0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3d404c7880>"}, "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": 1679235249689096427, "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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1085.434353710468, "std_reward": 258.71739542734633, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-19T15:14:50.612545"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d15cc05cbf0fd75210dafbf0085ae90bda531503178f6d8d23e92755f5457c0d
+size 2136