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: 1507.98 +/- 375.09
+      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:fa4bb671f0557cc598ad5d38348409e576792e3284497ec04089ce845a1bb15d
+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 0x7f58f63d4dc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f58f63d4e50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f58f63d4ee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f58f63d4f70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f58f63d8040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f58f63d80d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f58f63d8160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f58f63d81f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f58f63d8280>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f58f63d8310>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f58f63d83a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f58f63d8430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f58f63d6d00>"
+    },
+    "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": 1680441196134080223,
+    "learning_rate": 0.00095,
+    "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:": "gAWVNQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJbAAQAAAAAAAAAAAADTW5m2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACA79BxPQAAAAA+k+u/AAAAAK8CB74AAAAA8HXyPwAAAAD22IE5AAAAAJs+AEAAAAAAooM/PAAAAABjwO6/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5u8ztgAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgDuPBD4AAAAAIWL6vwAAAAD9ryw9AAAAAJaA2T8AAAAAvRgtPQAAAAAkyOY/AAAAAGhpmj0AAAAAsbPcvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALnanTYAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIDpaYq8AAAAAMf25L8AAAAAmLTmPQAAAACxsuU/AAAAALZZ9T0AAAAAj7/zPwAAAAD6zDo9AAAAAMFy4r8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB1Wl6yAACAPwAAAAAAAAAAAAAAAAAAAAAAAACA+SF2ugAAAABanOu/AAAAAG75C74AAAAAPGz2PwAAAABlCsc9AAAAAHyB/j8AAAAANukTvQAAAABvd+6/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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e64d1219835311932c044d64192c5e1a3356b4d274063842bb03a4ed6f86fb1
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:26c05905101f7fa84d348bec7f6e3f86abc39d6aca67dad395d8e87ff27a077e
+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 0x7f58f63d4dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f58f63d4e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f58f63d4ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f58f63d4f70>", "_build": "<function ActorCriticPolicy._build at 0x7f58f63d8040>", "forward": "<function ActorCriticPolicy.forward at 0x7f58f63d80d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f58f63d8160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f58f63d81f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f58f63d8280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f58f63d8310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f58f63d83a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f58f63d8430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f58f63d6d00>"}, "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": 1680441196134080223, "learning_rate": 0.00095, "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-glibc2.31 # 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": 1507.976542527246, "std_reward": 375.0906357377403, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-02T14:28:10.674839"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76009f95979e82071661da25a53073086167c15a6e0cbceb90d9be96372f3c06
+size 2136