Advantage Actor Critic for AntBulletEnv

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 1308.56 +/- 23.62
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+---
+
+# **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

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+version https://git-lfs.github.com/spec/v1
+oid sha256:31faa5e3604a5bf72faa7c0ad300cd75f65bb45f5c949dd8a53eaeb35ceecfb3
+size 129323

a2c-AntBulletEnv-v0/_stable_baselines3_version

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+1.8.0a2

a2c-AntBulletEnv-v0/data

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+{
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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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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7183e30b80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7183e30c10>",
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+        "forward": "<function ActorCriticPolicy.forward at 0x7f7183e30d30>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7183e30dc0>",
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+        "_predict": "<function ActorCriticPolicy._predict at 0x7f7183e30ee0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7183e30f70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7183db2040>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7183db20d0>",
+        "__abstractmethods__": "frozenset()",
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a2c-AntBulletEnv-v0/policy.optimizer.pth

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a2c-AntBulletEnv-v0/system_info.txt

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+- OS: Linux-5.15.0-58-generic-x86_64-with-glibc2.29 # 64~20.04.1-Ubuntu SMP Fri Jan 6 16:42:31 UTC 2023
+- Python: 3.8.10
+- Stable-Baselines3: 1.8.0a2
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.23.3
+- Gym: 0.21.0

config.json

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results.json

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