bqzhu commited on
Commit
c8cf8ca
1 Parent(s): ab43ea3
README.md ADDED
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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: -644.53 +/- 178.14
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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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+ # **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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+
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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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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 0x7f8bfa4773a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8bfa477430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8bfa4774c0>", 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+ - OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
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+ - Python: 3.9.16
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+ {"mean_reward": -644.5331588841975, "std_reward": 178.1365724910863, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-26T15:12:22.942358"}