Learning in minority games with multiple resources

Authors:
David Catteeuw;Bernard Manderick
Affiliations:
Computational Modeling Lab, Vrije Universiteit Brussel, Brussels, Belgium;Computational Modeling Lab, Vrije Universiteit Brussel, Brussels, Belgium
Venue:
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Year:
2009

Citing 4
Cited 0

Technical Note: \cal Q-Learning

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Abstract

We study learning in Minority Games (MG) with multiple resources. The MG is a repeated conflicting interest game involving a large number of agents. So far, the learning mechanisms studied were rather naive and involved only exploitation of the best strategy at the expense of exploring new strategies. Instead, we use a reinforcement learning method called Q-learning and show how it improves the results on MG extensions of increasing difficulty.