Cooperative Multi-Agent Systems Using Distributed Reinforcement Learning Techniques

Abstract

In this paper, the fully cooperative multi-agent system is studied, in which all of the agents share the same common goal. The main difficulty in such systems is the coordination problem: how to ensure that the individual decisions of the agents lead to jointly optimal decisions for the group? Firstly, a multi-agent reinforcement learning algorithm combining traditional Q-learning with observation-based teammate modeling techniques, called TM_Qlearning, is presented and evaluated. Several new cooperative action selection strategies are then suggested to improve the multi-agent coordination and accelerate learning, especially in the case of unknown and temporary dynamic environments. The effectiveness of combining TM_Qlearning with the new proposals is demonstrated using the hunting game.