Combining independent and joint learning: a negotiation based approach

This work presents a new class of multiagent reinforcement learning algorithms that takes advantage of negotiation in order to improve the process of action selection. In this class of algorithms, agents use communication to cooperate and negotiate over the joint actions, thus enhancing the process of action selection. In this paper a new algorithm in this class is proposed: the Negotiation-based Q-Learning (NQL), which uses negotiation in the context of the Q-Learning algorithm. Results show that allowing negotiation between agents significantly enhances the performance of the multiagent learning process.