Abstract
We study how a group of adaptive agents can coordinate when competing for limited resources. A popular game theoretic model for this is the Minority Game. In this article we show that the coordination among learning agents can improve when agents use different learning parameters or even evolve their learning parameters. Better coordination leads to less resources being wasted and agents achieving higher individual performance. We also show that learning algorithms which achieve good results when all agents use that same algorithm, may be outcompeted when directly confronting other learning algorithms in the Minority Game.
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Catteeuw, D., Manderick, B. (2012). Heterogeneous Populations of Learning Agents in the Minority Game. In: Vrancx, P., Knudson, M., Grześ, M. (eds) Adaptive and Learning Agents. ALA 2011. Lecture Notes in Computer Science(), vol 7113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28499-1_7
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DOI: https://doi.org/10.1007/978-3-642-28499-1_7
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