Amirarsalan-sn/Lunar-Lander
Lunar Lander envitoment of gymnasium solved using Double DQN and D3QN
This project explores various methods to teach an AI agent how to land a virtual spacecraft safely on a lunar surface. It takes in the spacecraft's state (like position and velocity) and outputs actions (like firing thrusters) to guide it. Anyone interested in training AI for autonomous navigation or control tasks would find this useful.
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Use this if you are a reinforcement learning practitioner or researcher looking for practical examples and solutions to common challenges when training agents for continuous control environments.
Not ideal if you need a plug-and-play solution for a real-world robotics problem without any background in reinforcement learning or Python.
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Language
Python
License
MIT
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Last pushed
Jun 10, 2024
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