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JMLR
2006

Collaborative Multiagent Reinforcement Learning by Payoff Propagation

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Collaborative Multiagent Reinforcement Learning by Payoff Propagation
In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of coordination graphs of Guestrin, Koller, and Parr (2002a) which exploits the dependencies between agents to decompose the global payoff function into a sum of local terms. First, we deal with the single-state case and describe a payoff propagation algorithm that computes the individual actions that approximately maximize the global payoff function. The method can be viewed as the decision-making analogue of belief propagation in Bayesian networks. Second, we focus on learning the behavior of the agents in sequential decision-making tasks. We introduce different model-free reinforcementlearning techniques, unitedly called Sparse Cooperative Q-learning, which approximate the global action-value function based on the topology of a coordination graph, and perform updates using the contribution of the individual ag...
Jelle R. Kok, Nikos A. Vlassis
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JMLR
Authors Jelle R. Kok, Nikos A. Vlassis
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