抄録
In this paper, we propose a method to diminish the state space explosion problem of a multiagent reinforcement learning context, where each agent needs to observe other agents' states, and previous actions at each step of its learning process. However, both the number of state and action become exponential in the number of agents, leading to enormous amount of computation and very slow learning. In our method, the agent considers other agents' statuses only when they interfere with one another to reach their goals. Our idea is that each agent starts with its state space which does not include information of others'. Then, they automatically expand and refine their state space when agents detect interference. We adopt the information theory measure of entropy to detect the interference status where agents should take into account the other agents. We demonstrate the advantage of our method over the properties of global convergence in a time efficient manner.
