Reinforcement Learning Approaches to Coordination in Cooperative Multi-agent Systems

We report on an investigation of reinforcement learning techniques for the learning of coordination in cooperative multi-agent systems. Specifically, we focus on two novel approaches: one is based on a new action selection strategy for Q-learning [10], and the other is based on model estimation with a shared action-selection protocol. The new techniques are applicable to scenarios where mutual observation of actions is not possible. To date, reinforcement learning approaches for such independent agents did not guarantee convergence to the optimal joint action in scenarios with high miscoordination costs. We improve on previous results [2] by demonstrating empirically that our extension causes the agents to converge almost always to the optimal joint action even in these difficult cases.