Transcription of Reinforcement Learning: An Introduction
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Reinforcement Learning: An Introduction
IReinforcement Learning: An IntroductionSecond edition, in progressRichard S. Sutton and Andrew G. Bartoc 2014, 2015A Bradford BookThe MIT PressCambridge, MassachusettsLondon, EnglandiiIn memory of A. Harry KlopfContentsPreface ..viiiSeries Forward ..xiiSummary of Notation ..xiii1 The Reinforcement learning Reinforcement learning .. Examples .. Elements of Reinforcement learning .. Limitations and Scope .. An Extended Example: Tic-Tac-Toe .. Summary .. History of Reinforcement learning .. Bibliographical Remarks ..25I Tabular Solution Methods272 Multi-arm Ann-Armed Bandit Problem .. Action-Value Methods .. Incremental Implementation .. Tracking a Nonstationary Problem .. Optimistic Initial Values .. Upper-Confidence-Bound Action Selection .. Gradient Bandits.
This book can also be used as part of a broader course on machine learning, arti cial intelligence, or neural networks. In this case, it may be desirable to cover only a subset of the material. We recommend covering Chapter 1 for a brief overview, Chapter 2 through Section 2.2, Chapter 3 except Sections 3.4,
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