Introduction to Reinforcement Learning

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Introduction to Reinforcement LearningCS 294-112: Deep Reinforcement LearningSergey LevineClass 1 is due next Wednesday! Remember that Monday is a holiday, so no office to start forming final project groups Final project assignment document and ideas document releasedToday s of a Markov decision of Reinforcement Learning of a RL overview of RL algorithm types Goals: Understand definitions & notation Understand the underlying Reinforcement Learning objective Get summary of possible algorithmsDefinitions1. run away2. ignore3. petTerminology & notationImages: Bojarskiet al. 16, NVIDIAtrainingdatasupervisedlearningImit ation LearningReward functionsDefinitionsAndrey MarkovDefinitionsAndrey MarkovRichard BellmanDefinitionsAndrey MarkovRichard BellmanDefinitionsThe goal of Reinforcement learningwe ll come back to partially observed laterThe goal of Reinforcement learningThe goal of Reinforcement learningFinite horizon case: state-action marginalstate-action marginalInfinite horizon case: stationary distributionstationary distributionstationary = the same before and after transitionInfinite horizon case: stationary distributionstationary distributionstationary = the same before and after transitionExpectations and stochastic syst

Introduction to Reinforcement Learning CS 294-112: Deep Reinforcement Learning Sergey Levine. Class Notes 1. Homework 1 is due next Wednesday! •Remember that Monday is a holiday, so no office hours 2. Remember to start forming final project groups •Final project assignment document and ideas document released.

  Introduction, Learning, Reinforcement, Reinforcement learning