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Introduction to Reinforcement Learning

Introduction to Reinforcement LearningJ. Zico KolterCarnegie Mellon University1 Agent interaction with environmentAgentEnvironmentActionaReward rStates2Of course, an oversimplification3 Review: 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)4 Some important quantities in MDPs(Deterministic) policy : : mapping from states to actions(Stochastic) policy.

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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