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: 1346.83 +/- 53.98
+      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 sha256:78db0da12ced63824ec9d39ac898ad483dc27fb7abe60f71e21c0f2db7f41837
+size 129242

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 0x7d80c69b5fc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d80c69b6050>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d80c69b60e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d80c69b6170>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d80c69b6200>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d80c69b6290>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d80c69b6320>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d80c69b63b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d80c69b6440>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d80c69b64d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d80c69b6560>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d80c69b65f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d80c69b9680>"
+    },
+    "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": 1690511173075149887,
+    "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:af3a79d1404d317dcb403a9a10884b005701398502674c216b2db38a92aa1226
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:453bf440072dc2b6fafe9e6051e885c145e315f58ebbeeb0c5f5c9199ad993bb
+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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Fri Jul 21 03:39:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0
+- GPU Enabled: True
+- Numpy: 1.23.5
+- 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 0x7d80c69b5fc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d80c69b6050>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d80c69b60e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d80c69b6170>", "_build": "<function ActorCriticPolicy._build at 0x7d80c69b6200>", "forward": "<function ActorCriticPolicy.forward at 0x7d80c69b6290>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d80c69b6320>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d80c69b63b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d80c69b6440>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d80c69b64d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d80c69b6560>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d80c69b65f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d80c69b9680>"}, "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": 1690511173075149887, "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:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAABhQce2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAjrgkPQAAAAANT+6/AAAAAIG3lTwAAAAA8FDePwAAAACzQAu+AAAAAMiY/T8AAAAAGzu0PQAAAAANB96/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALjxNgAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgFzo/b0AAAAApX7rvwAAAADwQ/M9AAAAAHMC4D8AAAAAXDVuPQAAAABtWvk/AAAAAMF/hLoAAAAABtTcvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACXJZjUAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIA7JQ2+AAAAAENc3b8AAAAAwNsJvgAAAAAX6ds/AAAAAP+Vsr0AAAAAm3z2PwAAAACbBee9AAAAAB3i6r8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAChZGO2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAZV7VvQAAAABDge+/AAAAAO6Ifr0AAAAA5bYAQAAAAAD9ec+9AAAAAAbz3j8AAAAA58auPQAAAABbVN6/AAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////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:": "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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:": "gAWVpQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5RoC0sIhZSMAUOUdJRSlIwEaGlnaJRoEyiWIAAAAAAAAAAAAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP5RoC0sIhZRoFnSUUpSMDWJvdW5kZWRfYmVsb3eUaBMolggAAAAAAAAAAQEBAQEBAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYIAAAAAAAAAAEBAQEBAQEBlGgiSwiFlGgWdJRSlIwKX25wX3JhbmRvbZROdWIu", "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Fri Jul 21 03:39:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0", "GPU Enabled": "True", "Numpy": "1.23.5", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc9b89492d8ac3fd57d58b9454e1497e0b40198bfacd8ce6120b1963a15845d8
+size 1154984

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1346.8295295399178, "std_reward": 53.981965249816525, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-28T04:29:38.966784"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d55c0cedffdc78d019e4ea3d71e9a07faa30c55bfa9db54d97ab187ef55d44d5
+size 2176