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Introduction to Reinforcement Learning

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Introduction to Reinforcement LearningJ. Zico KolterCarnegie Mellon University1Agent interaction with environmentAgentEnvironmentActionaReward rStates2Of course, an oversimplification3Review: Markov decision processRecall a (discounted) Markov decision process = , , , , : set of states : set of actions [0,1]: transition probability distribution , : rewards function, ( )is reward for state : discount factorThe RL twist: we don t know or , or they are too big to enumerate (only have the ability to act in the MDP, observe states and actions)4Some important quantities in MDPs(Deterministic) policy : : mapping from states to actions(Stochastic) policy : 0,1.

Introduction to Reinforcement Learning J. Zico Kolter Carnegie Mellon University 1. Agent interaction with environment Agent Environment States Rewardr Actiona 2. Of course, an oversimplification 3. Review: Markov decision process Recall a (discounted) Markov decision process ℳ=",#,$,%,&

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