Collaborative Multiagent Reinforcement Learning by Payoff Propagation

Collaborative Multiagent Reinforcement Learning by Payoff Propagation (2006)

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by Jelle R. Kok , Nikos Vlassis

Venue:	JOURNAL OF MACHINE LEARNING RESEARCH
Citations:	21 - 2 self

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Datum	Value	Source
TITLE	Collaborative Multiagent Reinforcement Learning by Payoff Propagation	user correction
AUTHOR NAME	Jelle R. Kok	user correction
AUTHOR NAME	Nikos Vlassis	user correction
ABSTRACT	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 agents to the maximal global action value. The combined use of an edge-based decomposition of the action-value function and the payoff propagation algorithm for efficient action selection, result in an approach that scales only linearly in the problem size. We provide experimental evidence that our method outperforms related multiagent reinforcement-learning methods based on temporal differences.	user correction - Legacy Corrections
YEAR	2006	INFERENCE
VENUE	JOURNAL OF MACHINE LEARNING RESEARCH	user correction
VENUE TYPE	JOURNAL	INFERENCE
PAGES	1789--1828	user correction
VOLUME	7	INFERENCE
CITATIONS	54 found	ParsCit 1.0