Hedged learning: regret-minimization with learning experts

Authors:
Yu-Han Chang;Leslie Pack Kaelbling
Affiliations:
Massachusetts Institute of Technology, Cambridge, MA;Massachusetts Institute of Technology, Cambridge, MA
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

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Abstract

In non-cooperative multi-agent situations, there cannot exist a globally optimal, yet opponent-independent learning algorithm. Regret-minimization over a set of strategies optimized for potential opponent models is proposed as a good framework for deciding how to behave in such situations. Using longer playing horizons and experts that learn as they play, the regret-minimization framework can be extended to overcome several shortcomings of earlier approaches to the problem of multi-agent learning.