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Title: Autonomous Racing Gameplay via Reinforcement Learning
Authors: Sedillo, Cody
Advisors: Appel, Andrew
Department: Computer Science
Class Year: 2020
Abstract: In this paper, I describe a successful application of reinforcement learning (RL) to a futuristic racing video game called F-Zero: Maximum velocity. The implementation relies on OpenAI Gym, a toolkit for developing and comparing RL algorithms. Gym Retro, a component of Gym, allows us to turn video games into environments suitable for RL. This presents an opportunity to evaluate the game integration process in an effort to expand the size of the library. The new environments are tested with baseline algorithms to ensure that the integration files provide for stable performance.
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Computer Science, 1988-2020

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