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: 804.32 +/- 73.77
+      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 sha256:56844f945aedd8ae7b5da1d5c7f501e2289de686e1633b23b2d1a71cc5cbf553
+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 0x7f7dc39cb550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7dc39cb5e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7dc39cb670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7dc39cb700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7dc39cb790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f7dc39cb820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7dc39cb8b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7dc39cb940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7dc39cb9d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7dc39cba60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7dc39cbaf0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7dc39cbb80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f7dc39d0600>"
+    },
+    "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": 1681652399046370086,
+    "learning_rate": 0.0009,
+    "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": 0.95,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.45,
+    "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 sha256:0e704550a1b0c5717764ac4ae1acba7a3d2ef97631c37f4e52f5bb548285bcb2
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84e7d9abc4ee4fe80ff7a4a49ec3bf567a8a0bfed0e915c7a7bb482c63f4d26f
+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 0x7f7dc39cb550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7dc39cb5e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7dc39cb670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7dc39cb700>", "_build": "<function ActorCriticPolicy._build at 0x7f7dc39cb790>", "forward": "<function ActorCriticPolicy.forward at 0x7f7dc39cb820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7dc39cb8b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7dc39cb940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7dc39cb9d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7dc39cba60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7dc39cbaf0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7dc39cbb80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f7dc39d0600>"}, "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": 1681652399046370086, "learning_rate": 0.0009, "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": 0.95, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.45, "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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 804.3195960666314, "std_reward": 73.7709865632134, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-16T14:41:53.576628"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1aa8fd5ae1788cafe9ce55bd081b123882988678afe06c18ab7272df48c7cf20
+size 2163