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.
