Off-Beat Multi-Agent Reinforcement Learning

Wei Qiu, Weixun Wang, Rundong Wang, Bo An, Yujing Hu, Svetlana Obraztsova, Zinovi Rabinovich, Jianye Hao, Yingfeng Chen, Changjie Fan

Abstract: We investigate cooperative multi-agent reinforcement learning in environments with off-beat actions, i.e., all actions have execution durations. During execution durations, the environmental changes are not synchronised with action executions. To learn efficient multi-agent coordination in environments with off-beat actions, we propose a novel reward redistribution method built on our novel graph-based episodic memory. We name our solution method as LeGEM. Empirical results on stag-hunter game show that it significantly boosts multi-agent coordination.