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.
