Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) by Richard S. Sutton, Andrew G. Barto
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) by Richard S. Sutton, Andrew G. Barto PDF, ePub eBook D0wnl0ad
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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