Multi-Agent Reinforcement Learning with Adaptive Mimetism

Abstract

Reinforcement learning is a framework in which an autonomous agent optimizes its bahavior by progressively improving its performance based on given rewards from the environment. Although several fruitful achievement has been made for the purpose of single-agent-adaptation by this framework, they are not applicable for multiple agents. To learn cooperatively, a new idea of reinforcement learning for multiple agents is needed. This paper describes a new method called Cooperative Reinforcement Learning with Spontaneous Mimetism where multiple agents in the environment learn cooperatively. First, we discuss two major problems of mimetism; when and whom to imitate. Next we compare Simple Mimetism where an agent always imitates on finding another agent in its neighborhood with simple reinforcement learning. To take advantages of both methods, we propose Adaptive Mimetism that adapts learning mode with balancing reinforcement learning and mimetism probabilistically by adjusting mimetism rate according to the situation. Finally, we show the merits of our method by the results of the simulation on the transportation problem in which several robots transport loads in the factory.