Upload 6 files
Browse files- README.md +37 -0
- config.json +1 -0
- gitattributes.txt +34 -0
- ppo-LunarLander-v2Morgan.zip +3 -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: 248.49 +/- 23.28
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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 0x7ff8747af670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff8747af700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff8747af790>", 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gitattributes.txt
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ppo-LunarLander-v2Morgan.zip
ADDED
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replay.mp4
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results.json
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{"mean_reward": 248.4883157158353, "std_reward": 23.28492142123289, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-16T23:15:27.853116"}
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