Switching dynamics of multi-agent learning

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Switching dynamics of multi-agent learning
This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcement learning agents and replicator dynamics in stateless multi-agent games. More precisely, in this work we use a combination of replicator dynamics and switching dynamics to model multi-agent learning automata in multi-state games. This is the first time that the dynamics of problems with more than one state is considered with replicator equations. Previously, it was unclear how the replicator dynamics of stateless games had to be extended to account for multiple states. We use our model to visualize the basin of attraction of the learning agents and the boundaries of switching dynamics at which an agent possibly arrives in a new dynamical system. Our model allows to analyze and predict the behavior of the different learning agents in a wide variety of multi-state problems. In our experiments we illustrate thi...
Peter Vrancx, Karl Tuyls, Ronald L. Westra
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2008
Where ATAL
Authors Peter Vrancx, Karl Tuyls, Ronald L. Westra
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