The Implementation of Asynchronous Advantage Actor-Critic with Stigmergy in Network-assisted Multi-agent System

Abstract

Multi-agent system (MAS) needs to mobilize multiple simple agents to complete complex tasks. However, it is difficult to coherently coordinate distributed agents by means of limited local information. In this paper, we propose a decentralized collaboration method named as "stigmergy" in network-assisted MAS, by exploiting digital pheromones (DP) as an indirect medium of communication and utilizing deep reinforcement learning (DRL) on top. Correspondingly, we implement an experimental platform, where KHEPERA IV robots form targeted specific shapes in a decentralized manner. Experimental results demonstrate the effectiveness and efficiency of the proposed method. Our platform could be conveniently extended to investigate the impact of network factors (e.g., latency, data rate, etc) on the level of collective intelligence.