Cooperative Multi-Agent Systems Using Distributed Reinforcement Learning Techniques

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. (C) 2018 The Authors. Published by Elsevier Ltd.