Reinforcement Learning, second edition: An Introduction

Description

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.

Get the book

Amazon.com

Search the book

eBay.com

Search the book

Similar books

• 

  Reinforcement Learning: An Introduction

  By Richard S. Sutton, Andrew G. Barto

  An Introduction Richard S. Sutton, Andrew G. Barto. extending the initial portions of trajectories that have ... Any simulated trajectory will pass through the tree and then exit it at some leaf node. Outside the tree and at the leaf ...

• 

  Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

  By Maxim Lapan

  Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL

• 

  Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

  By Maxim Lapan

  This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.

• 

  Reinforcement Learning

  By Phil Winder Ph.D.

  This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and ...

• 

  Grokking Deep Reinforcement Learning

  By Miguel Morales

  About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques.

• 

  Reinforcement Learning: State-of-the-Art

  By Marco Wiering, Martijn van Otterlo

  As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning.

• 

  Algorithms for Reinforcement Learning

  By Csaba Szepesvári

  This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming.

• 

  Foundations of Deep Reinforcement Learning: Theory and Practice in Python

  By Laura Graesser, Wah Loon Keng

  [125] Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. “Mastering the Game of Go with Deep Neural Networks and Tree Search.

• 

  Deep Reinforcement Learning: Fundamentals, Research and Applications

  By Hao Dong, Zihan Ding, Shanghang Zhang

  Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL.

• 

  Reinforcement Learning and Dynamic Programming Using Function Approximators

  By Lucian Busoniu, Robert Babuska, Bart De Schutter

  Dynamic multidrug therapies for HIV: Optimal and STI control approaches. Mathematical Biosciences and Engineering, 1(2):223–241. Antos, A., Munos, R., and Szepesv ́ari, Cs. (2008a). Fitted Q-iteration in continuous action-space MDPs.