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2003
Springer

On a Dynamical Analysis of Reinforcement Learning in Games: Emergence of Occam's Razor

9 years 4 months ago
On a Dynamical Analysis of Reinforcement Learning in Games: Emergence of Occam's Razor
Modeling learning agents in the context of Multi-agent Systems requires an adequate understanding of their dynamic behaviour. Usually, these agents are modeled similar to the different players in a standard game theoretical model. Unfortunately traditional Game Theory is static and limited in its usefelness. Evolutionary Game Theory improves on this by providing a dynamics which describes how strategies evolve over time. In this paper, we discuss three learning models whose dynamics are related to the Replicator Dynamics(RD). We show how a classical Reinforcement Learning(RL) technique, i.e. Qlearning relates to the RD. This allows to better understand the learning process and it allows to determine how complex a RL model should be. More precisely, Occam’s Razor applies in the framework of games, i.e. the simplest model (Cross) suffices for learning equilibria. An experimental verification in all three models is presented.
Karl Tuyls, Katja Verbeeck, Sam Maes
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where CEEMAS
Authors Karl Tuyls, Katja Verbeeck, Sam Maes
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