Algorithms for reinforcement learning [electronic resource]
- Responsibility
- Csaba Szepesvári.
- Imprint
- San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2010.
- Physical description
- 1 electronic text (xii, 89 p.) : ill.
- Series
- Synthesis digital library of engineering and computer science.
- Synthesis lectures on artificial intelligence and machine learning ; #9. 1939-4616
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Szepesvári, Csaba.
Contents/Summary
- Bibliography
- Includes bibliographical references (p. 73-88).
- Contents
-
- Markov Decision Processes Value Prediction Problems Control For Further Exploration.
- (source: Nielsen Book Data)
- Publisher's 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.
(source: Nielsen Book Data)
Subjects
- Subjects
- Reinforcement learning > Mathematical models.
- 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
Bibliographic information
- Publication date
- 2010
- Series
- Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; #9
- Note
- Part of: Synthesis digital library of engineering and computer science.
- Title from PDF t.p. (viewed on July 13, 2010).
- Series from website.
- Access
- Abstract freely available; full-text restricted to subscribers or individual document purchasers.
- Referenced in
- Compendex
- INSPEC
- Google scholar
- Google book search
- Note
- Also available in print.
- Format
- Mode of access: World Wide Web.
- System requirements: Adobe Acrobat Reader.
- ISBN
- 9781608454938 (electronic bk.)
- 9781608454921 (pbk.)
- 1608454932 (ebook)
- DOI
- 10.2200/S00268ED1V01Y201005AIM009
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