JohDonald/Deep-Q-Learning-Deep-SARSA-LunarLander-v2
Applying deep reinforcement learning algorithms, Deep SARSA and Deep Q-Learning, to OpenAI Gym's LunarLander-v2.
This project explores how to teach an AI to safely land a lunar module in a simulated environment. It takes data like the module's position and velocity, along with whether its legs are touching the ground, and produces a trained AI that knows the best actions to take (like firing thrusters) to land successfully. This is useful for researchers and students working on reinforcement learning and autonomous control.
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Use this if you are a researcher or student interested in understanding and applying deep reinforcement learning algorithms like Deep Q-Learning and Deep SARSA to a control problem.
Not ideal if you are looking for a plug-and-play solution for a real-world autonomous landing system or a different type of AI problem.
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Apr 09, 2021
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