Approximate Action Selection For Large, Coordinating, Multiagent Systems
Scott Sosnowski · OhioLink ETD Center (Ohio Library and Information Network) · 2016
Many practical decision-making problems involve coordinating teams of agents.In our work, we focus on the problem of coordinated action selection in reinforcement learning with large stochastic multi-agent systems that are centrally controlled.Previous work has shown how to formulate coordination as exact inference in a Markov network, but this becomes intractable for large teams of agents.We investigate the idea of "approximate coordination" as a solution to an approximate inference problem in a Markov network.We look at a pursuit domain and a simplified real-time strategy game and find that in these situations, such approaches are able to find good policies when exact approaches become intractable.