Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-1_LunarLander-v2.zip +3 -0
- ppo-1_LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-1_LunarLander-v2/data +95 -0
- ppo-1_LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-1_LunarLander-v2/policy.pth +3 -0
- ppo-1_LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-1_LunarLander-v2/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: 245.98 +/- 20.16
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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. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7fbd9dfcd280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbd9dfcd310>", "reset_noise": 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            oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
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            size 431
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        ppo-1_LunarLander-v2/system_info.txt
    ADDED
    
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            - 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
    
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        results.json
    ADDED
    
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            +
            {"mean_reward": 245.97838712933594, "std_reward": 20.161468159326393, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-05T12:27:05.380150"}
         | 
