Graphical models in continuous domains for multiagent reinforcement learning

Authors:
Scott Proper;Kagan Tumer
Affiliations:
Oregon State University, Corvallis, OR, USA;Oregon State University, Corvallis, OR, USA
Venue:
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Year:
2013

Citing 2
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Abstract

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.