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 .
Reinforcement learning has gradually become one of the most active research areas in machine learning, arti cial intelligence, and neural net-work research. The eld has developed strong mathematical foundations and impressive applications. The computational study of reinforcement learning is
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