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Author Szepesvári, Csaba.

Title Algorithms for reinforcement learning / Csaba Szepesvári
Published [San Rafael, Calif.] : Morgan & Claypool Publishers, [2010]
Online access available from:
Synthesis Digital Library    View Resource Record  


Description 1 online resource (xii, 89 pages) : illustrations
Series Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; #9
Synthesis lectures on artificial intelligence and machine learning ; #9
Contents 1. Markov decision processes -- Preliminaries -- Markov decision processes -- Value functions -- Dynamic programming algorithms for solving MDPs
Summary 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 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
Analysis Active learning
Actor-critic methods
Bias-variance tradeoff
Function approximation
Least-squares methods
Markov Decision Processes
Monte-Carlo methods
Natural gradient
Online learning
Policy gradient
Reinforcement learning
Simulation optimization
Stochastic approximation
Stochastic gradient methods
Temporal difference learning
Two-timescale stochastic approximation
Bibliography Includes bibliographical references (pages 73-88)
Subject Machine learning.
Markov processes.
Reinforcement learning -- Mathematical models.
Form Electronic book
ISBN 1608454932 (electronic bk.)
9781608454938 (electronic bk.)