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Reinforcement Learning - Lecture 1: Introduction

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Reinforcement LearningLecture 1: IntroductionAlexandre Proutiere, Sadegh Talebi, Jungseul OkKTH, The Royal Institute of TechnologyLecture 1: Outline1. Generic models for sequential decision making2. Overview and schedule of the course2Lecture 1: models for sequential decision making2. Overview and schedule of the course3Sequential Decision a sequential action selection / control policymaximising rewards4Sequential Decision MakingProblem definition1. System dynamics2. Set of available policies available information or feedback to thedecision maker3. Reward structure5Applications6Sequential Decision few examples: Linear:st+1=Ast+Bat Deterministic and stationary:st+1=F(st,at) Markovian:P(st+1=s |ht,st=s,at=a) =pt(s |s,a)where s pt(s |s,a) = 1; homogenous ifpt(s |s,a) =p(s |s,a)7Sequential Decision MakingInformation - Set of few examples: Markov Decision Process (MDP)- Fully observable state and reward- Known reward distribution and tr

Reinforcement Learning Lecture 1: Introduction Alexandre Proutiere, Sadegh Talebi, Jungseul Ok KTH, The Royal Institute of Technology

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