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Reinforcement Learning: Theory and Algorithms

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Reinforcement Learning: Theory and AlgorithmsAlekh AgarwalNan JiangSham M. KakadeWen SunJanuary 31, 2022WORKING DRAFT:Please any typos or errors you appreciate it!iiContents1Fundamentals31 Markov Decision (Infinite-Horizon) Markov Decision Processes . . . . . . . . . . . . . . . . . . . . . . . objective, policies, and values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Consistency Equations for Stationary Policies . . . . . . . . . . . . . . . . . . . . . Optimality Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Markov Decision Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complexity.

Reinforcement Learning: Theory and Algorithms Alekh Agarwal Nan Jiang Sham M. Kakade Wen Sun November 11, 2021 WORKING DRAFT: We will be frequently updating the book this fall, 2021. Please email bookrltheory@gmail.com with any typos or errors you find. We appreciate it!

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