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: 1562.37 +/- 74.97
+      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 sha256:6ad3e7cc4e395e474f09953cd76caf1a90067dc60437351d9698d0bb2cb5e612
+size 129231

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 0x7fd150165670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd150165700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd150165790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd150165820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fd1501658b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fd150165940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd1501659d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd150165a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fd150165af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd150165b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd150165c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd150165ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fd150166bc0>"
+    },
+    "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": 1682577591275511285,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAANTQsjyHDjQ/qsGVPd4lLj9e+7I/MVSnP+w/mD+qc0o+dLbmPVPGdb+Qi789MsYdwGRuXjs4upE/dVWlv6wRIj4TjvO8kdu3vh2wPD+Wtji8U1abP7gy1b4sAL+98mTYPiKGgL92Mgc/X3KsPjJwKD9dTq8+CJ54PuZA7T5ryG4/g4GdPxcDqz+1S5Y/yod4PSc5wzzucU+/Az7MvrcC2L8ilsU+PmJPP/MSor9MfeQ+Nu3HPqt+Ur/w3zw/1LgRPd3TmD9Vmi6/YGYmPpWHAj8ihoC/djIHP19yrD4ycCg/JTo0P1TTM745uSc/YE0PQE8MsT8ahS8/zAMIPyF4ob+7eo4/ukviPXNLpj7VE6S/01gnP49r6z7d5Zc+sF6lP0hdGz8eFEi/SC08P/REjDtXZv6+GdrMv1swcz8glgs+IoaAv3YyBz9fcqw+MnAoP8iVML6az5w+QDDYPulkRD//spK/xOFkP+Qa+D4gVe++bl5+v6OFCEBC8pc/BuDuPmuIQD62x9q/1cAPP+8hXz9vmoK/nX0MP/IcPz+GNuy+Usy9vp1JJUD/d4W/OFlAPtT0fj89X/K/X3KsPkWKwr+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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6066f40669e13edec045b59b0c40bf0771008506d2c276b00e5b810ff0b71d71
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f87eced1f77212a00efbaf0ca1d63afea4e2d1dc38d04de4b19ad2f36cb2cb9f
+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-5.10.147+-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 0x7fd150165670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd150165700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd150165790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd150165820>", "_build": "<function ActorCriticPolicy._build at 0x7fd1501658b0>", "forward": "<function ActorCriticPolicy.forward at 0x7fd150165940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd1501659d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd150165a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd150165af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd150165b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd150165c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd150165ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fd150166bc0>"}, "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": 1682577591275511285, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAANTQsjyHDjQ/qsGVPd4lLj9e+7I/MVSnP+w/mD+qc0o+dLbmPVPGdb+Qi789MsYdwGRuXjs4upE/dVWlv6wRIj4TjvO8kdu3vh2wPD+Wtji8U1abP7gy1b4sAL+98mTYPiKGgL92Mgc/X3KsPjJwKD9dTq8+CJ54PuZA7T5ryG4/g4GdPxcDqz+1S5Y/yod4PSc5wzzucU+/Az7MvrcC2L8ilsU+PmJPP/MSor9MfeQ+Nu3HPqt+Ur/w3zw/1LgRPd3TmD9Vmi6/YGYmPpWHAj8ihoC/djIHP19yrD4ycCg/JTo0P1TTM745uSc/YE0PQE8MsT8ahS8/zAMIPyF4ob+7eo4/ukviPXNLpj7VE6S/01gnP49r6z7d5Zc+sF6lP0hdGz8eFEi/SC08P/REjDtXZv6+GdrMv1swcz8glgs+IoaAv3YyBz9fcqw+MnAoP8iVML6az5w+QDDYPulkRD//spK/xOFkP+Qa+D4gVe++bl5+v6OFCEBC8pc/BuDuPmuIQD62x9q/1cAPP+8hXz9vmoK/nX0MP/IcPz+GNuy+Usy9vp1JJUD/d4W/OFlAPtT0fj89X/K/X3KsPkWKwr+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-5.10.147+-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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d49427582072143b1115e73cadc1da56c797381659962987793a1d2964396def
+size 1113281

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1562.3724741205108, "std_reward": 74.9702888579602, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-27T08:07:51.448662"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:379b85e657a4fb6c80eceb92760b956ea58c19af061a7307b04c5a0dae5fd3a4
+size 2170