Event-Driven MARL for Collaborative Swarm Confrontation in Asynchronous Environments

Abstract

Multi-agent reinforcement learning (MARL) provides a flexible solution for tackling task and motion planning challenges, particularly in swarm confrontation scenarios. By customizing termination conditions for diverse tasks, event-driven MARL reduces decision jitter caused by frequent task switching. However, it hinders robots from updating strategies on a consistent timescale, leading to misaligned information sharing that disrupts agent coordination. To address this, we propose a novel event-driven MARL approach that facilitates collaborative strategy learning under asynchronous conditions. The approach introduces an experience selection scheme tailored to diverse timescales, ensuring efficient training through synchronized information sharing among robots. By incorporating Transformers, our method enables robots to infer others' behaviors from historical data, optimizing collaborative strategies. Extensive experiments validate the effectiveness of our proposed approach.