Multi-agent reinforcement learning: an approach based on the other agent's internal model

In a multi-agent environment, whether one agent's action is good or not depends on the other agents' actions. In traditional reinforcement learning methods, which assume stationary environments, it is hard to take into account of the other agent's actions which may change due to learning. In this article, we consider a two-agent cooperation problem, and propose a multi-agent reinforcement learning method based on estimation of the other agent's actions. In our learning method, one agent estimates the other agent's action based on the internal model of the other agent. The internal model is acquired by the observation of the other agent's actions. Through experiments, we demonstrate that good cooperative behaviors are achieved by our learning method.