Multi-agent reinforcement learning with directed exploration and selective memory reuse
Shuo Jiang, Christopher Amato
Abstract
Many tasks require cooperation and coordination of multiple agents. Multi-agent reinforcement learning (MARL) can effectively learn solutions to these problems, but exploration and local optima problems are still open research topics. In this paper, we propose a new multi-agent policy gradient method called decentralized exploration and selective memory policy gradient (DecESPG) that addresses these issues. DecESPG consists of two additional components built on policy gradient: 1) an exploration bonus component that directs agents to explore novel observations and actions and 2) a selective memory component that records past trajectories to reuse valuable experience and reinforce cooperative behavior. Experimental results verify that the proposed method learns faster and outperforms state-of-the-art MARL algorithms.