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 Title Algorithms for Reinforcement Learning
 Author(s) Csaba Szepesvari
 Publisher: Morgan and Claypool, 1 edition (2010); eBook (Draft, Last update on March 12, 2019)
 Paperback: 104 pages
 eBook: PDF
 Language: English
 ISBN10: 1608454924
 ISBN13: 9781608454921
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Book Description
Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a longterm objective.What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions.
Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations.
Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering.
In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
About the Authors Csaba Szepesvari is a Professor of Computing Science at University of Alberta.
 Machine Learning
 Algorithms and Data Structures
 Neural Networks and Depp Learning
 Artificial Intelligence
 Statistics, Mathematical Statistics, and SAS Programming
 Probability and Stochastic Processe