inital commit - Trained PPO agent
Browse files- README.md +37 -0
 - config.json +1 -0
 - ppo_lunar.zip +3 -0
 - ppo_lunar/_stable_baselines3_version +1 -0
 - ppo_lunar/data +95 -0
 - ppo_lunar/policy.optimizer.pth +3 -0
 - ppo_lunar/policy.pth +3 -0
 - ppo_lunar/pytorch_variables.pth +3 -0
 - ppo_lunar/system_info.txt +7 -0
 - replay.mp4 +0 -0
 - results.json +1 -0
 
    	
        README.md
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            ---
         
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            library_name: stable-baselines3
         
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            tags:
         
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            - LunarLander-v2
         
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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: PPO
         
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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: LunarLander-v2
         
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                  type: LunarLander-v2
         
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                metrics:
         
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                - type: mean_reward
         
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                  value: 261.00 +/- 12.55
         
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                  name: mean_reward
         
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                  verified: false
         
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            ---
         
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            # **PPO** Agent playing **LunarLander-v2**
         
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            This is a trained model of a **PPO** agent playing **LunarLander-v2**
         
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            using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
         
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            ## Usage (with Stable-baselines3)
         
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            TODO: Add your code
         
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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
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It allows to keep variance\n        above zero and prevent it from growing too fast. 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| 90 | 
         
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     | 
| 91 | 
         
            +
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     | 
| 92 | 
         
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     | 
| 93 | 
         
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     | 
| 94 | 
         
            +
                "target_kl": null
         
     | 
| 95 | 
         
            +
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     | 
    	
        ppo_lunar/policy.optimizer.pth
    ADDED
    
    | 
         @@ -0,0 +1,3 @@ 
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            version https://git-lfs.github.com/spec/v1
         
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    ADDED
    
    | 
         @@ -0,0 +1,3 @@ 
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         | 
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            version https://git-lfs.github.com/spec/v1
         
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| 3 | 
         
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        ppo_lunar/pytorch_variables.pth
    ADDED
    
    | 
         @@ -0,0 +1,3 @@ 
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         | 
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            version https://git-lfs.github.com/spec/v1
         
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            oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
         
     | 
| 3 | 
         
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            size 431
         
     | 
    	
        ppo_lunar/system_info.txt
    ADDED
    
    | 
         @@ -0,0 +1,7 @@ 
     | 
|
| 
         | 
|
| 
         | 
|
| 
         | 
|
| 
         | 
|
| 
         | 
|
| 
         | 
|
| 
         | 
| 
         | 
|
| 1 | 
         
            +
            - OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
         
     | 
| 2 | 
         
            +
            - Python: 3.8.10
         
     | 
| 3 | 
         
            +
            - Stable-Baselines3: 1.7.0
         
     | 
| 4 | 
         
            +
            - PyTorch: 1.13.1+cu116
         
     | 
| 5 | 
         
            +
            - GPU Enabled: True
         
     | 
| 6 | 
         
            +
            - Numpy: 1.22.4
         
     | 
| 7 | 
         
            +
            - Gym: 0.21.0
         
     | 
    	
        replay.mp4
    ADDED
    
    | 
         Binary file (227 kB). View file 
     | 
| 
         | 
    	
        results.json
    ADDED
    
    | 
         @@ -0,0 +1 @@ 
     | 
|
| 
         | 
| 
         | 
|
| 1 | 
         
            +
            {"mean_reward": 261.0004938850142, "std_reward": 12.547116361887525, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-03T20:30:53.843664"}
         
     |