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Reinforcement Learning - An Introduction (Hardcover, second edition)
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Reinforcement Learning - An Introduction (Hardcover, second edition)
Series: Reinforcement Learning
Expected to ship within 9 - 15 working days
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The significantly expanded and updated new edition of a widely used
text on reinforcement learning, one of the most active research
areas in artificial intelligence. Reinforcement learning, one of
the most active research areas in artificial intelligence, is a
computational approach to learning whereby an agent tries to
maximize the total amount of reward it receives while interacting
with a complex, uncertain environment. In Reinforcement Learning,
Richard Sutton and Andrew Barto provide a clear and simple account
of the field's key ideas and algorithms. This second edition has
been significantly expanded and updated, presenting new topics and
updating coverage of other topics. Like the first edition, this
second edition focuses on core online learning algorithms, with the
more mathematical material set off in shaded boxes. Part I covers
as much of reinforcement learning as possible without going beyond
the tabular case for which exact solutions can be found. Many
algorithms presented in this part are new to the second edition,
including UCB, Expected Sarsa, and Double Learning. Part II extends
these ideas to function approximation, with new sections on such
topics as artificial neural networks and the Fourier basis, and
offers expanded treatment of off-policy learning and
policy-gradient methods. Part III has new chapters on reinforcement
learning's relationships to psychology and neuroscience, as well as
an updated case-studies chapter including AlphaGo and AlphaGo Zero,
Atari game playing, and IBM Watson's wagering strategy. The final
chapter discusses the future societal impacts of reinforcement
learning.
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