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Book Description
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,