Interaction Models for Multiagent Reinforcement Learning

This article proposes and compares different interaction models for reinforcement learning based on multi-agent system. The cooperation during the learning process is crucial to guarantee the convergence to a good policy. The exchange of rewards among the agents during the interaction is a complex task and if it is inadequate it may cause delays in learning or generate unexpected transitions, making the cooperation inefficient and con-verging to a non-satisfactory policy. In order to allow the interactive discovery of high quality policies we have developed several cooperation models based on the ex-change of action policies between the agents. Experimental results have shown that the proposed cooperation models are able to speed up the convergence of the agents while achieving optimal action policies even in high-dimensional environments (e.g. traffic), outperforming the standard Q-learning algorithm.