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: 1176.83 +/- 160.27
+      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 sha256:75f540cc51ac150ba4095bf5db8e77f22f626b05b45909ade5fe6c9b48b854a5
+size 129244

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 0x7f095d1e9120>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f095d1e91b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f095d1e9240>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f095d1e92d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f095d1e9360>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f095d1e93f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f095d1e9480>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f095d1e9510>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f095d1e95a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f095d1e9630>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f095d1e96c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f095d1e9750>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f08fbf5fc00>"
+    },
+    "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": 1684662804379133743,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8d4fee8e5c6d63e151c626ff8ec47da0dd276cd260dcb32648058b194ea2365
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f80d095294830d679749ad969c1ac4810502db82ce473c8ad93eee2bcc767b1f
+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: 3.10.11
+- 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 0x7f095d1e9120>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f095d1e91b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f095d1e9240>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f095d1e92d0>", "_build": "<function ActorCriticPolicy._build at 0x7f095d1e9360>", "forward": "<function ActorCriticPolicy.forward at 0x7f095d1e93f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f095d1e9480>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f095d1e9510>", "_predict": "<function ActorCriticPolicy._predict at 0x7f095d1e95a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f095d1e9630>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f095d1e96c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f095d1e9750>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f08fbf5fc00>"}, "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": 1684662804379133743, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAJsk9T0sQGG/8O1ivrOznb9zQeE+9J8dPhzlhj41kZY/70jAvygblL8yniC+yi5TvjlMcb9i3ec+Yc06vxeLz71fWIo/suYbQFF6H78cHzC/y+zjPu3sbj8z8dy+QLBbvnHx/D6A4Jo+LEgDP7msYj9RZKI//YWrvye7nr9Z3Ms/W+bHv64IBUBTmX+/G5Zcv6ZcUr7trgNAg2RMP89m0DpHGDc/su4wQNF3Hz5BVpe+FkSdv0AjKL8Tzae/2p13PwCKnz93Swy9PXscPxrt7b9x8fw+gOCaPixIAz9Bj5C/HK2APxeuBb9PBSM+wP/APj2KYL7Xq5s/sd0FP800cL7lnty/+3qYPipy4j9H7tE9XqOcPw0CgT6p6yq/629rv0uAzT9yhFs+USo8vxtvhT87cZg/mwKOv4rkUD8n04dAcfH8PoDgmj4sSAM/QY+Qv7bhLT/n27G/omrXv4Exhz94BJq/mYGLP9jbLz0qkaQ/whPVv6QZTz6bvu++SHejvnwu+z5nuBXAA3u6vwc8yj8x2t0/bExkPVmvQ79O+rK8yz6kP08W3b6jkV2+02CePwKMAcCA4Jo+qJn5v7msYj+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, "_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": "3.10.11", "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:b3f2bd41a24660301e7bd63be82e11719bd7a1799208cc38020a06fe17656705
+size 1118638

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1176.828873818089, "std_reward": 160.26596383158721, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-21T11:01:06.091305"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88ec70361de8eca68f2f70a35b99e4b2fbdcfd940da12926ac81d57090f4c6a3
+size 2176