Relation-Aware Learning for Multitask Multiagent Cooperative Games

Collaboration among multiple tasks is advantageous for enhancing learning efficiency in multiagent reinforcement learning. To guide agents in cooperating with different teammates in multiple tasks, contemporary approaches encourage agents to exploit common cooperative patterns or identify the learning priorities of multiple tasks. Despite the progress made by these methods, they all assume that all cooperative tasks to be learned are related and desire similar agent policies. This is rarely the case in multiagent cooperation, where minor changes in team composition can lead to significant variations in cooperation, resulting in distinct cooperative strategies compete for limited learning resources. In this article, to tackle the challenge posed by multitask learning in potentially competing cooperative tasks, we propose a novel framework called relation-aware learning (RAL). RAL incorporates a relation awareness module in both task representation and task optimization, aiding in reasoning about task relationships and mitigating negative transfers among dissimilar tasks. To assess the performance of RAL, we conduct a comparative analysis with baseline methods in a multitask StarCraft environment. The results demonstrate the superiority of RAL in multitask cooperative scenarios, particularly in scenarios involving multiple conflicting tasks.