# Show Reference: "Reinforcement Learning: An Introduction"

Reinforcement Learning: An Introduction (01 March 1998) by Richard S. Sutton, Andrew G. Barto
@book{sutton-and-barto-1998,
abstract = {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.},
author = {Sutton, Richard S. and Barto, Andrew G.},
day = {01},
howpublished = {Hardcover},
isbn = {0262193981},
keywords = {learning, reinforcement-learning},
month = mar,
posted-at = {2014-01-08 10:28:51},
priority = {2},