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README.md +37 -0
a2c-PandaReachDense-v3.zip +3 -0
a2c-PandaReachDense-v3/_stable_baselines3_version +1 -0
a2c-PandaReachDense-v3/data +97 -0
a2c-PandaReachDense-v3/policy.optimizer.pth +3 -0
a2c-PandaReachDense-v3/policy.pth +3 -0
a2c-PandaReachDense-v3/pytorch_variables.pth +3 -0
a2c-PandaReachDense-v3/system_info.txt +9 -0
config.json +1 -0
replay.mp4 +0 -0
results.json +1 -0
vec_normalize.pkl +3 -0

README.md ADDED Viewed

	@@ -0,0 +1,37 @@

+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.23 +/- 0.11
+      name: mean_reward
+      verified: false
+---
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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-PandaReachDense-v3.zip ADDED Viewed

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+oid sha256:4471daf2b635217e4a7d0a153750efcc99b8e09fc11b9c5cb2190e1669e0949b
+size 113166

a2c-PandaReachDense-v3/_stable_baselines3_version ADDED Viewed

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1	+ 2.1.0

a2c-PandaReachDense-v3/data ADDED Viewed

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+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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: Windows-10-10.0.22621-SP0 10.0.22621
+- Python: 3.11.4
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.0.1
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.26.2

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