• 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
• ISBN-10: 1608454924
• ISBN-13: 978-1608454921
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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 long-term objective.What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions.

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.

• Csaba Szepesvari is a Professor of Computing Science at University of Alberta.

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• Machine Learning
• Algorithms and Data Structures
• Neural Networks and Depp Learning
• Artificial Intelligence
• Statistics, Mathematical Statistics, and SAS Programming
• Probability and Stochastic Processe

• Algorithms for Reinforcement Learning (Csaba Szepesvari)
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