Information Theoretic Approach for Measuring Interaction in Multiagent Domain
Sachiyo Arai, Yoshihisa Ishigaki · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2009
Although a large number of reinforcement learning algorithms have been proposed for the generation of cooperative behaviors, the question of how to evaluate mutual benefit or loss among them is still open. As far as we know, an emerged behavior is regarded as a cooperative behavior when embedded agents have finally achieved their global goal, regardless of whether or not mutual interference has had any effect during the course of the learning process of each agent. Thus, we cannot detect any harmful interaction on the way to achieving a fully-converged policy. In this paper, we propose a measure based on information theory for evaluating the degree of interaction during the learning process from the viewpoint of information sharing. In order to discuss the bad effects of concurrent learning, we apply our proposed measure to a situation in which there exist conflicts among the agents, and we show the availability of our measure.