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: 2164.04 +/- 80.36
+      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:d6c0dfa4a4321010cdf3f089252fad7916753fc91c07ddace7caad3f972803f6
+size 129260

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 0x7f810738fe50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f810738fee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f810738ff70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8107394040>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f81073940d0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8107394160>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f81073941f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8107394280>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8107394310>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f81073943a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8107394430>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f81073944c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f810738d900>"
+    },
+    "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": 1674617087653335157,
+    "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 sha256:7c22cc6d049f1dda2336cf5519e65eba5a6722b026432147bf7be7905a8f9ecc
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:895df2a6e46a7ff8ddec01d1a6c356abd28da5d17f6ff6bac1a86e9a1137b891
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- 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 0x7f810738fe50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f810738fee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f810738ff70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8107394040>", "_build": "<function ActorCriticPolicy._build at 0x7f81073940d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f8107394160>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f81073941f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8107394280>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8107394310>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f81073943a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8107394430>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f81073944c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f810738d900>"}, "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": 1674617087653335157, "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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7cad05a2ffc9716e5c061262647234e2c51a0a808090a250569a3732bab0558
+size 1287463

results.json

@@ -0,0 +1 @@
+{"mean_reward": 2164.043095494248, "std_reward": 80.3626693248609, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-25T04:15:41.041385"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:257835070f743a4462bd6ae44e89fa09291bda73a6c88a01ed4c41c3bdce2370
+size 2136