Algorithms for Reinforcement Learning Csaba Szepesvari

ISBN: 9781608454938

Published: August 8th 2010

ebook

103 pages


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Algorithms for Reinforcement Learning  by  Csaba Szepesvari

Algorithms for Reinforcement Learning by Csaba Szepesvari
August 8th 2010 | ebook | PDF, EPUB, FB2, DjVu, AUDIO, mp3, RTF | 103 pages | ISBN: 9781608454938 | 8.65 Mb

Reinforcement learning 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 isMoreReinforcement learning 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 learners 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. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration



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