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Upload DQN Pong-v4 pretrained agent

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DQN-Pong_v4_v12.zip ADDED
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DQN-Pong_v4_v12/_stable_baselines3_version ADDED
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+ 2.2.1
DQN-Pong_v4_v12/data ADDED
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DQN-Pong_v4_v12/policy.optimizer.pth ADDED
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DQN-Pong_v4_v12/policy.pth ADDED
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DQN-Pong_v4_v12/pytorch_variables.pth ADDED
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DQN-Pong_v4_v12/system_info.txt ADDED
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+ - OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
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+ - Python: 3.10.12
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+ - Stable-Baselines3: 2.2.1
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+ - PyTorch: 2.2.1+cu121
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+ - GPU Enabled: True
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+ - Numpy: 1.25.2
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+ - Cloudpickle: 2.2.1
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+ - Gymnasium: 0.29.1
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+ - OpenAI Gym: 0.25.2
README.md ADDED
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - PongNoFrameskip-v4
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: DQN
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: PongNoFrameskip-v4
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+ type: PongNoFrameskip-v4
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+ metrics:
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+ - type: mean_reward
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+ value: -21.00 +/- 0.00
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **DQN** Agent playing **PongNoFrameskip-v4**
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+ This is a trained model of a **DQN** agent playing **PongNoFrameskip-v4**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+
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+ ## Usage (with Stable-baselines3)
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+ TODO: Add your code
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+
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+
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+ ```python
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+ from stable_baselines3 import ...
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+ from huggingface_sb3 import load_from_hub
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+
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+ ...
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+ ```
config.json ADDED
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replay.mp4 ADDED
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results.json ADDED
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+ {"mean_reward": -21.0, "std_reward": 0.0, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2024-03-31T11:18:13.313771"}