Initial commit

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: 1205.48 +/- 137.27
+      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:da33429341b417fffd6ffc15ac2392db00cfd6ca72a4de67f40b535304faadb3
+size 129265

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 0x7fdf0ecea4c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdf0ecea550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdf0ecea5e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdf0ecea670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fdf0ecea700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fdf0ecea790>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdf0ecea820>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdf0ecea8b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fdf0ecea940>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdf0ecea9d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdf0eceaa60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdf0eceaaf0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fdf0ecec2c0>"
+    },
+    "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": 1680012519487418080,
+    "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:cfe3fc34559a05474a1f606a5984431f560926175fd595eb5ee6dfbfebf5d4e2
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6c55ee72c38704e78eda4a76b5407f19a43b07f7eaa8f08aa857ecaf870e058
+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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- 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 0x7fdf0ecea4c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fdf0ecea550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fdf0ecea5e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fdf0ecea670>", "_build": "<function ActorCriticPolicy._build at 0x7fdf0ecea700>", "forward": "<function ActorCriticPolicy.forward at 0x7fdf0ecea790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fdf0ecea820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fdf0ecea8b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fdf0ecea940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fdf0ecea9d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fdf0eceaa60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fdf0eceaaf0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fdf0ecec2c0>"}, "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": 1680012519487418080, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAXcVb/MFJO/Z2bHvRZeYj9SlxK///PyPgBYED6fB4c+ZiNmP8HtAL/WVoW+Er2JPczco78mkUjAhcMDPwoK1b6KD2Q/k9+Zv+YYgz4ot86+wnV5v3j1Sj320gy/djOmvNnLFT/ZTUrABnMRP7pIub/0KFa/VvBLPw1V3D4KNUa+EUiIPUOq2z7sRog+YMuWPkpLhz+kfyG8YNsnvrwjOr1DS1O/8tlKwB806j6v+nS/CaPlPlVJtL8ZQRg/ssKSPnFrer9uO0k8vO0NvxVOa7zZyxU/2U1KwMFJ4b9N2jA/3oVavyc5Pz8MV+k+g8IJQNAwmT/ISIi/B+rQPNl65D44LoA/O+UAPqbY3L7/2rE6MsIxvyU7oz8MH6Y+7kffPgo0sz+az6O9nDsfP17bBz3Lznm/VAhAPbWc0r51y1a+G8Dav1X5oT7BSeG/TdowP8b8qb02lcG/9eEvvwL68z+hWYY/cAwUvgZN4T5wCWW/3sAJP2OHT7+sUze+M4CkPy+dGr7GjNi+TuwfPwAs07+h3Io/ILVIv+7aHz8/i189Rkp5v5FHtjxoFYy+LiAtwBvA2r9V+aE+BnMRP7pIub+UjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAADAtV22AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAlYEMvgAAAAAaSeG/AAAAADL7or0AAAAAOwTtPwAAAAAVbda9AAAAAMm+3D8AAAAAFonEPQAAAABmbem/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAxvOLNgAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgAH6nr0AAAAAwJD2vwAAAABHfGa8AAAAAMyz4z8AAAAAcw93PAAAAADfCec/AAAAAJwe0b0AAAAASjHkvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACBewbYAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIAfOa29AAAAAKSg+L8AAAAASGivvQAAAAA5XuE/AAAAABP/xr0AAAAAKgoBQAAAAABgiRE+AAAAAFcwAcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABpHsy1AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAZwspvQAAAACVoP+/AAAAAAzaZr0AAAAAW/foPwAAAACig829AAAAAMvH7T8AAAAAx1lSvQAAAAAvpue/AAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLHIaUjAFDlHSUUpQu"}, "_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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1205.4785036877438, "std_reward": 137.27463464036083, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-28T15:08:42.061043"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f746282fc7befcd3b8abd6a189c081b22b8cf3b6989cac769deec084f91159d5
+size 2136