{"id":569,"date":"2021-03-23T02:02:40","date_gmt":"2021-03-23T02:02:40","guid":{"rendered":"http:\/\/kb.shoelace.biz\/?p=569"},"modified":"2025-11-08T15:37:12","modified_gmt":"2025-11-08T15:37:12","slug":"reinforcement-learning-for-speed","status":"publish","type":"post","link":"https:\/\/kb.shoelace.biz\/?p=569","title":{"rendered":"Reinforcement Learning for Speed"},"content":{"rendered":"\n

I have recently written a paper on applying Q-Learning<\/a> – a reinforcement learning algorithm – to the acceleration problem brought up in my Going Fast on Your Own Power<\/em> series. The paper’s abstract, the paper itself, and a corresponding presentation are below.<\/p>\n\n\n\n

Abstract<\/h2>\n\n\n\n

In this report, Q-Learning is used to maximize velocity and minimize driver fatigue in a human powered vehicle attempting a land speed record. Q-Learning results in a set of Q-values for state-action pairs, called a policy. That policy is utilized here to provide a function of power input over time for a given driver, vehicle, and environment combination that maximizes velocity for the driver\u2019s fatigue limit.This function of power input over time results in higher achievable speeds than other methods of developing a power over time function.<\/p>\n\n\n\n

Paper<\/h2>\n\n\n\n
Q-Learning-for-Speed<\/a>Download<\/a><\/div>\n\n\n\n

Presentation<\/h2>\n\n\n\n
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