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A2C-Atari-Pong.zip

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A2C-Atari-Pong/_stable_baselines3_version

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+2.0.0

A2C-Atari-Pong/policy.optimizer.pth

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A2C-Atari-Pong/policy.pth

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A2C-Atari-Pong/pytorch_variables.pth

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A2C-Atari-Pong/system_info.txt

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+- OS: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Fri Jan 27 02:56:13 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0
+- PyTorch: 2.0.1+cu117
+- GPU Enabled: False
+- Numpy: 1.25.0
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.26.2

README.md

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+---
+library_name: stable-baselines3
+tags:
+- PongNoFrameskip-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PongNoFrameskip-v4
+      type: PongNoFrameskip-v4
+    metrics:
+    - type: mean_reward
+      value: -20.10 +/- 0.83
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PongNoFrameskip-v4**
+This is a trained model of a **A2C** agent playing **PongNoFrameskip-v4**
+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": -20.1, "std_reward": 0.8306623862918076, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2023-07-10T10:42:50.538178"}