A2C Training

README.md

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+---
+library_name: stable-baselines3
+tags:
+- ALE/Pong-v5
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: ALE/Pong-v5
+      type: ALE/Pong-v5
+    metrics:
+    - type: mean_reward
+      value: -19.80 +/- 3.60
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **ALE/Pong-v5**
+This is a trained model of a **A2C** agent playing **ALE/Pong-v5**
+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.zip

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a2c/_stable_baselines3_version

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+2.3.2

a2c/policy.optimizer.pth

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a2c/policy.pth

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a2c/pytorch_variables.pth

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a2c/system_info.txt

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+- OS: macOS-14.7-arm64-arm-64bit Darwin Kernel Version 23.6.0: Wed Jul 31 20:48:52 PDT 2024; root:xnu-10063.141.1.700.5~1/RELEASE_ARM64_T6020
+- Python: 3.9.19
+- Stable-Baselines3: 2.3.2
+- PyTorch: 2.4.0
+- GPU Enabled: False
+- Numpy: 2.0.2
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.1
+- OpenAI Gym: 0.26.2

results.json

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+{"mean_reward": -19.8, "std_reward": 3.6, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2024-09-27T20:21:45.357551"}