Sciweavers

ALDT
2009
Springer

Anytime Self-play Learning to Satisfy Functional Optimality Criteria

14 years 5 months ago
Anytime Self-play Learning to Satisfy Functional Optimality Criteria
We present an anytime multiagent learning approach to satisfy any given optimality criterion in repeated game self-play. Our approach is opposed to classical learning approaches for repeated games: namely, learning of equilibrium, Pareto-efficient learning, and their variants. The comparison is given from a practical (or engineering) standpoint, i.e., from a point of view of a multiagent system designer whose goal is to maximize the system’s overall performance according to a given optimality criterion. Extensive experiments in a wide variety of repeated games demonstrate the efficacy of our approach.
Andriy Burkov, Brahim Chaib-draa
Related Content
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ALDT
Authors Andriy Burkov, Brahim Chaib-draa
Comments (0)