{"author_name":"changlikesdesktop","height":"190","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/changlikesdesktop/20210218/20210218050921.png","description":"This article is a translation of Japanese ver. Original ver. is here*1. Hi, this is chang. Today I tried to make two agents of artificial intelligence learn bike road race through interactive competitions. 0. Bike road race Previously, I wrote that goal sprint using slipstream is a typical strategy \u2026","author_url":"https://blog.hatena.ne.jp/changlikesdesktop/","categories":["mechanical learning","Tensorflow"],"provider_name":"Hatena Blog","version":"1.0","url":"https://changlikesdesktop.hatenablog.com/entry/2021/03/05/145840","published":"2021-03-05 14:58:40","type":"rich","blog_url":"https://changlikesdesktop.hatenablog.com/","blog_title":"\u30aa\u30c3\u30b5\u30f3\u306fDesktop\u304c\u597d\u304d","width":"100%","provider_url":"https://hatena.blog","title":"AI bike racer learned observation, goal sprint, and break","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchanglikesdesktop.hatenablog.com%2Fentry%2F2021%2F03%2F05%2F145840\" title=\"AI bike racer learned observation, goal sprint, and break - \u30aa\u30c3\u30b5\u30f3\u306fDesktop\u304c\u597d\u304d\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>"}