Speed up reinforcement learning between two agents with adaptive mimetism

Abstract

To realize a speed up in learning without homogenizing the agents' behaviors in a multi-agent system, it is important to selectively share learning results. This paper describes a method designed to permit multiple agents to learn cooperatively. The advantage of our method is to dynamically switch the learning mode between mimetism and reinforcement learning according to the situation. Mimetism seeks stability in its behavior, while individual reinforcement leaning seeks the better solution. Accordingly, selective mimetism that allows the agents to partially share learning results,works to prevent homogenization among the agents. Experimental results are given for a ball-pushing task between the two virtual agents for evaluating the effectiveness of our method. This method will be useful for cooperative reinforcement learning with adaptive mimetism based on propagating the learned behaviors of a virtual agent to a physical robot in order to accelerate leaning in a physical environment.