一种基于分布式强化学习的多智能体协调方法

多智能体系统研究的重点在于使功能独立的智能体通过协商、协调和协作，完成复杂的控制任务或解决复杂的问题。通过对分布式强化学习算法的研究和分析，提出了一种多智能体协调方法，协调级将复杂的系统任务进行分解，协调智能体利用中央强化学习进行子任务的分配，行为级中的任务智能体接受各自的子任务，利用独立强化学习分别选择有效的行为，协作完成系统任务。通过在Robot Soccer仿真比赛中的应用和实验，说明了基于分布式强化学习的多智能体协调方法的效果优于传统的强化学习。

A Method for Multi-Agent Coordination Based on Distributed Reinforcement Learning

The emphasis of research on multi - agent system is that the individual agents apply their negotiation, coordination, and cooperation to accomplish the complicated task or resolve the complex problem. With analysis and research on distributed reinforcement learning, a method for multi - agent cooperation is proposed. Coordination level decomposes the complicated task and the central reinforcement learning is used to assign the subtask by coordination agent. In behavioral level, the task agents receive the sub - tasks and adopt the individual reinforcement to choose the effective action and accomplish global task cooperatively. With the application and experiment in Robot Soccer simulation game, this method shows better performance than that of the conventional reinforcement learning.