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README.md +37 -0
a2c-AntBulletEnv-v0.zip +3 -0
a2c-AntBulletEnv-v0/_stable_baselines3_version +1 -0
a2c-AntBulletEnv-v0/data +105 -0
a2c-AntBulletEnv-v0/policy.optimizer.pth +3 -0
a2c-AntBulletEnv-v0/policy.pth +3 -0
a2c-AntBulletEnv-v0/pytorch_variables.pth +3 -0
a2c-AntBulletEnv-v0/system_info.txt +7 -0
config.json +1 -0
results.json +1 -0
vec_normalize.pkl +3 -0

README.md ADDED Viewed

	@@ -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: 1675.33 +/- 79.88
+      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 ADDED Viewed

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a2c-AntBulletEnv-v0/_stable_baselines3_version ADDED Viewed

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1	+ 1.8.0

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+        "__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    ",
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+        "__abstractmethods__": "frozenset()",
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+- OS: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.27 # 1 SMP Fri Apr 2 22:23:49 UTC 2021
+- Python: 3.10.9
+- Stable-Baselines3: 1.8.0
+- PyTorch: 1.11.0+cu113
+- GPU Enabled: True
+- Numpy: 1.21.2
+- Gym: 0.21.0

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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 0x7f30787493f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3078749480>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3078749510>", 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