2004 PerformanceBoundedReinforcement

• (Banerjee & Peng, 2004) ⇒ Bikramjit Banerjee, and Jing Peng. (2004). “Performance Bounded Reinforcement Learning in Strategic Interactions.” In: Proceedings of the 19th national conference on Artifical intelligence. ISBN:0-262-51183-5

Subject Headings: ReDVaLeR Algorithm, Multi-Agent Reinforcement Learning Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222004%22+Performance+Bounded+Reinforcement+Learning+in+Strategic+Interactions
• http://dl.acm.org/citation.cfm?id=1597148.1597151&preflayout=flat#citedby

Quotes

Abstract

Despite increasing deployment of agent technologies in several business and industry domains, user confidence in fully automated agent driven applications is noticeably lacking. The main reasons for such lack of trust in complete automation are scalability and nonexistence of reasonable guarantees in the performance of selfadapting software. In this paper we address the latter issue in the context of learning agents in a Multiagent System (MAS). Performance guarantees for most existing on-line Multiagent Learning (MAL) algorithms are realizable only in the limit, thereby seriously limiting its practical utility. Our goal is to provide certain meaningful guarantees about the performance of a learner in a MAS, while it is learning. In particular, we present a novel MAL algorithm that (i) converges to a best response against stationary opponents, (ii) converges to a Nash equilibrium in self-play and (iii) achieves a constant bounded expected regret at any time (no-average-regret asymptotically) in arbitrary sized general-pum games with non-negative payoffs, and against any number of opponents.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 PerformanceBoundedReinforcement	Jing Peng Bikramjit Banerjee			Performance Bounded Reinforcement Learning in Strategic Interactions						2004