{"version":"1.0","provider_name":"Institute for Data Science","provider_url":"https:\/\/carleton.ca\/cuids","author_name":"cuthemeedtr5","author_url":"https:\/\/carleton.ca\/cuids\/author\/cuthemeedtr5\/","title":"Reinforcement Learning for Board Games, an Introduction (Decisive AI Inc.) - Institute for Data Science","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"vlC8z18q9O\"><a href=\"https:\/\/carleton.ca\/cuids\/event\/reinforcement-learning-for-board-games-an-introduction\/\">Reinforcement Learning for Board Games, an Introduction (Decisive AI Inc.)<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/carleton.ca\/cuids\/event\/reinforcement-learning-for-board-games-an-introduction\/embed\/#?secret=vlC8z18q9O\" width=\"600\" height=\"338\" title=\"&#8220;Reinforcement Learning for Board Games, an Introduction (Decisive AI Inc.)&#8221; &#8212; Institute for Data Science\" data-secret=\"vlC8z18q9O\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/carleton.ca\/cuids\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","description":"How can you create an agent that plays games? Using reinforcement learning, an agent can learn how to win by training against itself, rather than being told how to win by a programmer. In this seminar, we are going to cover the basics of reinforcement learning and how it was applied to create an expert [&hellip;]","thumbnail_url":"https:\/\/carleton.ca\/cuids\/wp-content\/uploads\/sites\/245\/Emiliano-Conde.png","thumbnail_width":504,"thumbnail_height":504}