2002 MultiagentLearningUsingaVariabl

• (Bowling & Veloso, 2002) ⇒ Michael Bowling, and Manuela Veloso. (2002). “Multiagent Learning Using a Variable Learning Rate.” In: Artificial Intelligence Journal, 136(2). doi:10.1016/S0004-3702(02)00121-2

Subject Headings: Multi-Agent Learning Algorithm; WoLF Algorithm.

Notes

• Alternative PDF Link(s):
  • https://www.ri.cmu.edu/pub_files/pub3/bowling_michael_2002_1/bowling_michael_2002_1.pdf
  • http://www.cs.cmu.edu/~mmv/papers/02aij-mike.pdf

Cited By

• http://scholar.google.com/scholar?q=%222002%22+Multiagent+Learning+Using+a+Variable+Learning+Rate

Quotes

Author Keywords

• Multiagent Learning; Reinforcement Learning; Game Theory

Abstract

Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents. This creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, Win or Learn Fast€, for varying the learning rate. We examine this technique theoretically, proving convergence in self-play on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of self-play and otherwise, demonstrating the wide applicability of this method.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2002 MultiagentLearningUsingaVariabl	Manuela Veloso Michael Bowling			Multiagent Learning Using a Variable Learning Rate				10.1016/S0004-3702(02)00121-2		2002