Title Algorithms for reinforcement learning / Csaba Szepesvári

Published Cham, Switzerland : Springer, ©2010

Online access available from: Synthesis Digital Library    View Resource Record

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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 Reinforcement learning Markov Decision Processes Temporal difference learning Stochastic approximation Two-timescale stochastic approximation Monte-Carlo methods Simulation optimization Function approximation Stochastic gradient methods Least-squares methods Overfitting Bias-variance tradeoff Online learning Active learning Planning Simulation PAC-learning Q-learning Actor-critic methods Policy gradient Natural gradient Bibliography Includes bibliographical references (pages 73-88) Subject Reinforcement learning -- Mathematical models Machine learning. Markov processes. Markov Chains Machine Learning COMPUTERS -- Enterprise Applications -- Business Intelligence Tools. COMPUTERS -- Intelligence (AI) & Semantics. Machine learning Markov processes Form Electronic book ISBN 9781608454938 1608454932 1608454924 9781608454921 9783031015519 3031015517

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