Diverse Collaboration in Multi-Agent Reinforcement Learning via Self-Adaptive Method

Abstract

Multi-Agent Reinforcement Learning (MARL) has shown significant promise in tackling complex cooperative tasks, largely due to parameter sharing among agents. However, while this sharing facilitates teamwork, it can also result in agent homogenization, which limits individualized behaviors. To address this issue, we introduce a novel method called Diverse Collaboration in Multi-Agent Reinforcement Learning via Self-Adaptive Method (DC-SA). DC-SA advances individualized behaviors by maximizing the mutual information between agents’ representations and their trajectories to enhance diversity collaboration. The method employs adaptive weights to balance collaboration and individualization, particularly in scenarios where collaboration is challenging. Our empirical results demonstrate that DC-SA outperforms five baselines on the StarCraft II micromanagement tasks.