Model-based learning of interaction strategies in multi-agent systems

Abstract

. Agents that operate in a multi-agent system need an efficient strategy to handle their encounters with other agents involved. Searching for an optimal interaction strategy is a hard problem because it depends mostly on the behaviour of the others. One way to deal with this problem is to endow the agents with the ability to adapt their strategies based on their interaction experience. This work views interaction as a repeated game and presents a general architecture for a model-based agent that learns models of the rival agents for exploitationin future encounters. First, we describe a method for inferring an optimal strategy against a given model of another agent. Second, we present an unsupervised algorithm that infers a model of the opponent's strategy from its interaction behaviour in the past. We then present a method for incorporating exploration strategies into model-based learning. We report experimental results demonstrating the superiority of the model-based learning agent over non-adaptive agents and over reinforcement-learning agents.