Initial Commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- HalfCheetahBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: HalfCheetahBulletEnv-v0
+      type: HalfCheetahBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 1556.13 +/- 16.29
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **HalfCheetahBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **HalfCheetahBulletEnv-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-HalfCheetahBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:864de032a32c8c9666e5244cb0b89be96de83fbfaf6cb7af1717ec39b65a092f
+size 132739

a2c-HalfCheetahBulletEnv-v0/_stable_baselines3_version

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

a2c-HalfCheetahBulletEnv-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 0x7f9f38d17790>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9f38d17820>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9f38d178b0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9f38d17940>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9f38d179d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f9f38d17a60>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9f38d17af0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9f38d17b80>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f9f38d17c10>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9f38d17ca0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9f38d17d30>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9f38d17dc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f9f38d18f40>"
+    },
+    "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": 5000192,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681867569093723552,
+    "learning_rate": 0.0003,
+    "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:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
+    },
+    "_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": -3.8399999999993994e-05,
+    "_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": 19532,
+    "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": [
+            26
+        ],
+        "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]",
+        "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]",
+        "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]",
+        "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]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            6
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 32
+}

a2c-HalfCheetahBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21837dfdb285239fed726c241ed4ba4b0fe59e0bb899d9bbcbd7058dc41a5ca3
+size 54142

a2c-HalfCheetahBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4132e5e3175e1211252f7352d0d67f4429bf64e45d45f7c81c7af0821b507afa
+size 54846

a2c-HalfCheetahBulletEnv-v0/pytorch_variables.pth

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

a2c-HalfCheetahBulletEnv-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: 1.8.0
+- PyTorch: 2.0.0+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 0x7f9f38d17790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9f38d17820>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9f38d178b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9f38d17940>", "_build": "<function ActorCriticPolicy._build at 0x7f9f38d179d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f9f38d17a60>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9f38d17af0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9f38d17b80>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9f38d17c10>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9f38d17ca0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9f38d17d30>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9f38d17dc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9f38d18f40>"}, "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": 5000192, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681867569093723552, "learning_rate": 0.0003, "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:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_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": -3.8399999999993994e-05, "_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": 19532, "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": [26], "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]", "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]", "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]", "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]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [6], "low": "[-1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True]", "_np_random": null}, "n_envs": 32, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1556.1327483213622, "std_reward": 16.29342830654481, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-19T02:46:52.031462"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4746c3dcca1b899fdb0b736fbf3bba6e4a443e64f19aeeff6c742fe4ea0dd925
+size 2090