juliankappler/lunar-lander
Implementation of deep reinforcement learning algorithms for training an agent to play the game lunar lander
This project helps machine learning researchers explore and compare deep reinforcement learning algorithms for training an agent to successfully land a lunar module. It takes in various algorithm parameters and outputs a trained agent capable of playing the Lunar Lander game, along with performance statistics. Researchers and students in AI or control systems would use this to understand algorithm trade-offs.
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Use this if you are a researcher or student interested in experimenting with and comparing Actor-Critic and Deep Q-Learning algorithms for a control task.
Not ideal if you need a production-ready solution for complex aerospace control or want to apply reinforcement learning to a different problem domain without modification.
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Last pushed
May 15, 2023
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