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nsanghi
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a2c-Atari-Pong

Reinforcement Learning
stable-baselines3
PongNoFrameskip-v4
deep-reinforcement-learning
Eval Results
Model card Files Community
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  1. A2C-Atari-Pong.zip +2 -2
  2. A2C-Atari-Pong/_stable_baselines3_version +1 -1
  3. A2C-Atari-Pong/data +0 -0
  4. A2C-Atari-Pong/policy.optimizer.pth +2 -2
  5. A2C-Atari-Pong/policy.pth +2 -2
  6. A2C-Atari-Pong/pytorch_variables.pth +2 -2
  7. A2C-Atari-Pong/system_info.txt +9 -9
  8. README.md +1 -1
  9. config.json +0 -0
  10. replay.mp4 +0 -0
  11. results.json +1 -1
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A2C-Atari-Pong/system_info.txt CHANGED
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- - OS: Linux-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
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- - Python: 3.10.12
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- - Stable-Baselines3: 2.0.0
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- - PyTorch: 2.0.1+cu118
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- - GPU Enabled: True
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- - Numpy: 1.22.4
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- - Cloudpickle: 2.2.1
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- - Gymnasium: 0.28.1
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- - OpenAI Gym: 0.25.2
 
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README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
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  type: PongNoFrameskip-v4
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  metrics:
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  - type: mean_reward
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- value: -20.30 +/- 1.27
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  name: mean_reward
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  verified: false
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  ---
 
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  type: PongNoFrameskip-v4
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  metrics:
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  name: mean_reward
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  verified: false
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  ---
config.json CHANGED
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replay.mp4 CHANGED
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results.json CHANGED
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- {"mean_reward": -20.3, "std_reward": 1.268857754044952, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2023-07-13T08:10:28.742344"}
 
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