pascal poupart ngenlsbdcsuwaterlooca apologies multiple postings icml07 tutorial bayesian methods reinforcement learning httpwwwcsuwaterloocappouparticml07tutorialbayesrlhtml corvallis oregon usa 20 june 2007 motivation although bayesian methods reinforcement learning traced back 1960s howards work operations research bayesian methods used sporadically modern reinforcement learning part nonbayesian approaches tend much simpler work however recent advances shown bayesian approaches need complex initially thought offer several theoretical advantages instance keeping track full distributions instead point estimates unknowns bayesian approaches permit comprehensive quantification uncertainty regarding transition probabilities rewards value function parameters policy parameters distributional information used optimize principled way classic explorationexploitation tradeoff speed learning process similarly active learning reinforcement learning naturally optimized estimation gradient performance respect value function andor policy parameters also done accurately using less data bayesian approaches also facilitate encoding prior knowledge explicit formulation domain assumptions primary goal tutorial raise awareness research community regard bayesian methods properties potential benefits advancement reinforcement learning outline 1 introduction reinforcement learning bayesian learning 2 history bayesian rl 3 modelbased bayesian rl 31 policy optimization techniques 32 encoding domain knowledge 33 explorationexploitation tradeoff active learning 34 bayesian imitation learning rl 35 bayesian multiagent coordination coalition formation rl 4 modelfree bayesian rl 41 gaussian process temporal difference gptd 42 gaussian process sarsa 43 bayesian policy gradient 44 bayesian actorcritic algorithms 5 demo 51 control octopus arm using gptd presenters pascal poupart university waterloo ppoupartatcsdotuwaterloodotca httpwwwcsuwaterloocappoupart pascal poupart received phd degree computer science university toronto 2005 since august 2004 assistant professor david r cheriton school computer science university waterloo pouparts research focuses design analysis scalable algorithms sequential decision making uncertainty including bayesian reinforcement learning application assistive technologies eldercare spoken dialogue management information retrieval served program committee several international conferences including aamas 2006 2007 uai 2005 2006 2007 icml 2007 aaai 2005 2006 2007 nips 2007 aistats 2007 mohammad ghavamzadeh university alberta mghatcsdotualbertadotca httpwwwcsualbertacamgh mohammad ghavamzadeh received phd degree computer science university massachusetts amherst 2005 since september 2005 postdoctoral fellow department computing science university alberta working prof richard sutton main objective research investigate principles scalable decisionmaking grounded realworld applications last two years ghavamzadehs research mostly focused using recent advances statistical machine learning especially bayesian reasoning kernel methods develop scalable reinforcement learning algorithms yaakov engel university alberta yakiatcsdotualbertadotca httpwwwcsualbertacayaki yaakov engel received phd degree hebrew university jerusalem 2005 since april 2005 postdoctoral fellow alberta ingenuity centre machine learning aicml department computing science university alberta pascal poupart assistant professor david r cheriton school computer science university waterloo 200 university avenue west waterloo ontario canada n2l 3g1 web httpwwwcsuwaterloocappoupart email ngenlsbdcsuwaterlooca telephone 15198884567x36239 fax 15198851208 thu 07 aug 2008 193132 0400 uai icml07 tutorial bayesian methods reinforcement learning