Optimization methods in fully cooperative scenarios: a review of multiagent reinforcement learning

Abstract

Multiagent reinforcement learning (MARL) has become a dazzling new star in the field of reinforcement learning in recent years, demonstrating its immense potential across many application scenarios. The reward function directs agents to explore their environments and make optimal decisions within them by establishing evaluation criteria and feedback mechanisms. Concurrently, cooperative objectives at the macro level provide a trajectory for agents’ learning, ensuring alignment between individual behavioral strategies and the overarching system goals. The interplay between reward structures and cooperative objectives not only bolsters the effectiveness of individual agents but also fosters interagent collaboration, offering both momentum and direction for the development of swarm intelligence and the harmonious operation of multiagent systems. This review delves deeply into the methods for designing reward structures and optimizing cooperative objectives in MARL, along with the most recent scientific advancements in this field. The article meticulously reviews the application of simulation environments in cooperative scenarios and discusses future trends and potential research directions in the field, providing a forward-looking perspective and inspiration for subsequent research efforts.

Keywords

Multiagent reinforcement learning (MARL) / Cooperative framework / Reward function / Cooperative objective optimization

RIGHTS & PERMISSIONS

Zhejiang University Press