2010 AlgorithmsforReinforcementLearn

• (Szepesvari, 2010) ⇒ Csaba Szepesvari. (2010). “Algorithms for Reinforcement Learning." Morgan and Claypool Publishers. ISBN:1608454924, 9781608454921 doi:10.2200/S00268ED1V01Y201005AIM009

Subject Headings: Reinforcement Learning Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222010%22+Algorithms+for+Reinforcement+Learning
• http://dl.acm.org/citation.cfm?id=1855083&preflayout=flat#citedby

Quotes

Abstract

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 AlgorithmsforReinforcementLearn	Csaba Szepesvari			Algorithms for Reinforcement Learning				10.2200/S00268ED1V01Y201005AIM009		2010