Transcription of Lecture 1: Introduction to Reinforcement Learning
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Lecture 1: Introduction to Reinforcement Learning
Lecture 1: Introduction to Reinforcement LearningLecture 1: Introduction to ReinforcementLearningDavid SilverLecture 1: Introduction to Reinforcement LearningOutline1 Admin2 About Reinforcement Learning3 The Reinforcement Learning Problem4 Inside An RL Agent5 Problems within Reinforcement LearningLecture 1: Introduction to Reinforcement LearningAdminClass InformationThursdays 9:30 to 11:00amWebsite: : me: 1: Introduction to Reinforcement LearningAdminAssessmentAssessment will be 50% coursework, 50% examCourseworkAssignment A: RL problemAssignment B: Kernels problemAssessment =max(assignment1,assignment2)Examination A: 3 RL questionsB: 3 kernels questionsAnswer any 3 questionsLecture 1: Introduction to Reinforcement LearningAdminTextbooksAn Introduction to Reinforcement Learning , Sutton andBarto, 1998 MIT Press, 1998 40 poundsAvailable free online!
A poker playing agent only observes public cards Now agent state 6= environment state Formally this is apartially observable Markov decision process (POMDP) Agent must construct its own state representation Sa t, e.g. Complete history: Sa t= H Beliefsof environment state: Sa t = (P[Se t = s1];:::;P[Se t = sn]) Recurrent neural network: S a t ...
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