Graphical models in continuous domains for multiagent reinforcement learning
Scott Proper, Kagan Tumer · 2013
In this paper we test two coordination methods – difference rewards and coordination graphs – in a continuous, multiagent rover domain using reinforcement learning, and discuss the situations in which each of these methods perform better alone or together, and why. We also contribute a novel method of applying coordination graphs in a continuous domain by taking advantage of the wire-fitting ap-proach used to handle continuous state and action spaces.