Learning to cooperate in multi-agent social dilemmas

Authors:
Enrique Munoz de Cote;Alessandro Lazaric;Marcello Restelli
Affiliations:
Politecnico di Milano, piazza Leonardo da Vinci, Milan, Italy;Politecnico di Milano, piazza Leonardo da Vinci, Milan, Italy;Politecnico di Milano, piazza Leonardo da Vinci, Milan, Italy
Venue:
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Year:
2006

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Abstract

In many Multi-Agent Systems (MAS), self-interested agents need to cooperate in order to maximize their own utilities in time. The goal of this work is to improve cooperation among agents that use best-response Reinforcement Learning (RL) algorithms (Q-Learning), by the introduction of two new principles (Change or Learn Fast and Change and Keep) that foster the reaching of Pareto efficient stable outcomes.