Reinforcement Learning: An Introduction 

Reinforcement Learning: An Introduction PDF Free Download

Book Description: 

The greatly 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 seeks to maximize the total amount of reward they receive while interacting with a complex and uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple overview of the key ideas and algorithms in the field. This second edition has been significantly expanded and updated, introducing new topics and updating the coverage of other topics.

Like the first edition, this second edition also focuses on the basic algorithms of online learning, with the more mathematical material highlighted in shaded boxes. Part I covers as much 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 the function approach, with new sections on topics such as artificial neural networks and Fourier bases, and offers a broader treatment of out-of-politics learning and political gradient methods. Part III contains new chapters on the relationship of reinforcement learning to psychology and neuroscience, as well as an updated case study chapter that includes AlphaGo and AlphaGo Zero, Atari games, and IBM Watson’s gaming strategy. The final chapter discusses the future social impacts of reinforcement learning.

 

Reinforcement Learning: An Introduction

Author(s): Richard S. Sutton, Andrew G. Barto

Series: Adaptive Computation and Machine Learning

Publisher: The MIT Press, Year: 2018

ISBN: 978-0262193986


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