{"id":574,"date":"2021-06-17T00:22:00","date_gmt":"2021-06-17T00:22:00","guid":{"rendered":"http:\/\/kb.shoelace.biz\/?p=574"},"modified":"2025-11-08T15:37:12","modified_gmt":"2025-11-08T15:37:12","slug":"physical-simulation-optimization","status":"publish","type":"post","link":"https:\/\/kb.shoelace.biz\/?p=574","title":{"rendered":"Physical Simulation Optimization"},"content":{"rendered":"\n<p>In my <a href=\"http:\/\/kb.shoelace.biz\/?p=569\">last post<\/a>, I applied a reinforcement learning algorithm called Q-Learning to maximize velocity in simulations attempting a human powered land speed record. This algorithm was limited by:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>An incomplete physics model.<\/li><li>A sub-optimal reward function.<\/li><li>A single driver limit.<\/li><\/ul>\n\n\n\n<p>Improving the algorithm and simulation in these three ways is the focus of this paper.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\n\n\n\n<p>In this paper a reinforcement learning algorithm called Q-Learning is applied 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 a function of power input over time for a given driver, vehicle, and environment combination that maximizes velocity for the driver&#8217;s fatigue limit. This function of power input over time results in higher achievable speeds than other methods of developing a power over time function. Several extensions of the Q-Learning algorithm are explored, along with a multi-driver vehicle simulation result.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Paper<\/h2>\n\n\n\n<div class=\"wp-block-file\"><a href=\"http:\/\/kb.shoelace.biz\/wp-content\/uploads\/2021\/06\/Physical-Simulation-Optimization-using-Q-Learning-1.pdf\">Physical-Simulation-Optimization-using-Q-Learning<\/a><a href=\"http:\/\/kb.shoelace.biz\/wp-content\/uploads\/2021\/06\/Physical-Simulation-Optimization-using-Q-Learning-1.pdf\" class=\"wp-block-file__button\" download>Download<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>In my last post, I applied a reinforcement learning algorithm called Q-Learning to maximize velocity in simulations attempting a human powered land speed record. This algorithm was limited by: An incomplete physics model. A sub-optimal reward function. A single driver limit. Improving the algorithm and simulation in these three ways is the focus of this&#8230;<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12,6],"tags":[],"class_list":["post-574","post","type-post","status-publish","format-standard","hentry","category-bicycling","category-optimization"],"_links":{"self":[{"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=\/wp\/v2\/posts\/574","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=574"}],"version-history":[{"count":1,"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=\/wp\/v2\/posts\/574\/revisions"}],"predecessor-version":[{"id":988,"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=\/wp\/v2\/posts\/574\/revisions\/988"}],"wp:attachment":[{"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=574"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=574"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kb.shoelace.biz\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=574"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}