{"id":768724,"date":"2021-08-30T16:52:41","date_gmt":"2021-08-30T23:52:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&#038;p=768724"},"modified":"2021-09-03T15:41:51","modified_gmt":"2021-09-03T22:41:51","slug":"project-galena","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-galena\/","title":{"rendered":"Project Galena"},"content":{"rendered":"<section class=\"mb-3 moray-highlight\">\n\t<div class=\"card-img-overlay mx-lg-0\">\n\t\t<div class=\"card-background  has-background-catalina-blue card-background--inset-right\">\n\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1173\" height=\"673\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/Galena-header-inset-graphic.jpg\" class=\"attachment-full size-full\" alt=\"Project Galena - flow illustration\" style=\"\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/Galena-header-inset-graphic.jpg 1173w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/Galena-header-inset-graphic-300x172.jpg 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/Galena-header-inset-graphic-1024x588.jpg 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/Galena-header-inset-graphic-768x441.jpg 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2021\/08\/Galena-header-inset-graphic-240x138.jpg 240w\" sizes=\"auto, (max-width: 1173px) 100vw, 1173px\" \/>\t\t<\/div>\n\t\t<!-- Foreground -->\n\t\t<div class=\"card-foreground d-flex mt-md-n5 my-lg-5 px-g px-lg-0\">\n\t\t\t<!-- Container -->\n\t\t\t<div class=\"container d-flex mt-md-n5 my-lg-5 align-self-center\">\n\t\t\t\t<!-- Card wrapper -->\n\t\t\t\t<div class=\"w-100 w-lg-col-5\">\n\t\t\t\t\t<!-- Card -->\n\t\t\t\t\t<div class=\"card material-md-card py-5 px-md-5\">\n\t\t\t\t\t\t<div class=\"card-body \">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\n<h1 id=\"project-galena\" class=\"h2\">Project Galena<\/h1>\n\n\n\n<p>Improving gaming with imitation learning<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n<p>In project Galena, we\u2019re asking how imitation learning can make gaming better.&nbsp;&nbsp;<\/p>\n\n\n\n<p>Imitation learning starts from demonstrations of how to do a task, or from a teacher who knows how to do it. By analyzing the demonstrations or by asking for examples from the teacher, we train an AI agent to do the same thing. The key benefit of imitation learning is that it lets us easily design and edit complex behaviors for AI agents: we just&nbsp;have to&nbsp;show the agent a few examples of what to do, instead of trying to tell the agent what to do by writing code or scripts.&nbsp;<\/p>\n\n\n\n<p>We\u2019re currently using imitation learning to help reduce the effects of lag in cloud gaming. When a player\u2019s connection is poor, it can take too long for actions, like joystick movements and button presses, to reach the game server, leading to a poor gaming experience. We are training an AI agent that can smooth over occasional lag problems by interpolating actions that aren\u2019t received in time.&nbsp;&nbsp;<\/p>\n\n\n\n<p>We\u2019re also looking at other uses of imitation learning. For example, imitation learning can help game designers develop believable non-player characters&#8211;imitation makes it easier to understand and edit complex non-player character (NPC) behaviors.<\/p>\n\n\n\n<h3 id=\"related-work\">Related work<\/h3>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-paidia\/\"><\/a><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-paidia\/\" target=\"_blank\" rel=\"noreferrer noopener\">Project Paidia: a Microsoft Research & Ninja Theory Collaboration<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/watch-for\/\">Watch For<\/a><\/li><li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-malmo\/\">Project Malmo<\/a><\/li><\/ul>\n\n\n","protected":false},"excerpt":{"rendered":"<p>Galena uses imitation learning to provide predictions of controller inputs to compensate for poor network conditions. If client-to-server lag occurs, predictions are used instead of user input.<\/p>\n","protected":false},"featured_media":769807,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-768724","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[768730],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Friederike Niedtner","user_id":39919,"people_section":"Section name 0","alias":"fniedtner"}],"msr_research_lab":[437514,1148609],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/768724","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":10,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/768724\/revisions"}],"predecessor-version":[{"id":770506,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/768724\/revisions\/770506"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/769807"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=768724"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=768724"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=768724"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=768724"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=768724"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}