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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Reinforcement Learning - An Introduction (Hardcover, second edition)
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Reinforcement Learning - An Introduction (Hardcover, second edition)
Series: Adaptive Computation and Machine Learning series
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Richard Sutton and Andrew Barto provide a clear and simple account
of the key ideas and algorithms of reinforcement learning. Their
discussion ranges from the history of the field's intellectual
foundations to the most recent developments and applications.
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 when interacting with a complex, uncertain environment. In
Reinforcement Learning, Richard Sutton and Andrew Barto provide a
clear and simple account of the key ideas and algorithms of
reinforcement learning. Their discussion ranges from the history of
the field's intellectual foundations to the most recent
developments and applications. The only necessary mathematical
background is familiarity with elementary concepts of probability.
The book is divided into three parts. Part I defines the
reinforcement learning problem in terms of Markov decision
processes. Part II provides basic solution methods: dynamic
programming, Monte Carlo methods, and temporal-difference learning.
Part III presents a unified view of the solution methods and
incorporates artificial neural networks, eligibility traces, and
planning; the two final chapters present case studies and consider
the future of reinforcement learning.
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