Itomigna2/Muesli-lunarlander
Muesli RL algorithm implementation (PyTorch) (LunarLander-v2)
This project offers a streamlined implementation of the Muesli reinforcement learning algorithm, designed to efficiently train agents for environments like LunarLander-v2. It takes in environment data (like observations from a game) and outputs a trained agent capable of performing actions to achieve a goal. This tool is for machine learning researchers and practitioners focused on developing and evaluating advanced reinforcement learning models with reduced computational costs.
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Use this if you are a reinforcement learning researcher looking for a Muesli algorithm implementation for continuous control tasks like the LunarLander-v2 environment.
Not ideal if you are a beginner looking for a simple reinforcement learning tutorial or if you are not comfortable with Docker and command-line interfaces for managing experiments.
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19
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Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 18, 2024
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