Learning Coordination in Multi-Agent Systems Using Influence Value Reinforcement Learning

Abstract

In this paper authors propose a new paradigm for learning coordination in multi-agent systems. This approach is based on social interaction of people, specially in the fact that people communicate each other what they think about their actions and this opinion can influence the behavior of each other. It is proposed a model in which agents, into a multi-agent system, learns to coordinate actions giving opinions about actions of other agents and also being influenced with opinions of other agents about their actions. This paradigm was used to develop a modified version of the Q-learning algorithm. This algorithm was tested and compared with independent learning (IL) and joint action learning (JAL) in two single state problems with two agents. This approach shows to have more probability to converge to an optimal equilibrium than IL and JAL Q-learning algorithms. Also, it does not need to make an entire model of all joint actions like JAL algorithms.