trained model for ALE/Galaxian-v5 using PPO

PPO-Galaxian-v5.zip

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PPO-Galaxian-v5/_stable_baselines3_version

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+2.3.2

PPO-Galaxian-v5/policy.optimizer.pth

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PPO-Galaxian-v5/policy.pth

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PPO-Galaxian-v5/pytorch_variables.pth

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PPO-Galaxian-v5/system_info.txt

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+- OS: macOS-14.5-arm64-arm-64bit Darwin Kernel Version 23.5.0: Wed May  1 20:19:05 PDT 2024; root:xnu-10063.121.3~5/RELEASE_ARM64_T8112
+- Python: 3.9.6
+- Stable-Baselines3: 2.3.2
+- PyTorch: 2.3.0
+- GPU Enabled: False
+- Numpy: 1.26.4
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.1

README.md

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+---
+library_name: stable-baselines3
+tags:
+- ALE/Galaxian-v5
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: ALE/Galaxian-v5
+      type: ALE/Galaxian-v5
+    metrics:
+    - type: mean_reward
+      value: 940.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **ALE/Galaxian-v5**
+This is a trained model of a **PPO** agent playing **ALE/Galaxian-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
+
+...
+```

results.json

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+{"mean_reward": 940.0, "std_reward": 0.0, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2024-05-21T12:57:31.252524"}