Graphical Models in Continuous Domains for Multiagent Reinforcement Learning

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 approach used to handle continuous state and action spaces.