基于过滤机制筛选信息的多智能体策略方法

Research on multi-agent strategy based on filtering mechanism to filter information

多智能体系统在进行协作或竞争时,会面临联合信息空间扩大、智能体间信息提取效率降低的问题.对此,采用增加过滤机制来筛选信息的多智能体强化学习策略方法(FMAC),以增强智能体间信息交流能力.该方法通过找到彼此相关联的智能体,根据相关性计算智能体的信息贡献,过滤掉无关智能体信息,从而实现在合作、竞争或者混合环境下智能体间有效的沟通.与此同时,采用集中训练分散执行的方式解决环境的非平稳性问题.通过对比算法进行实验,结果表明改进算法提高了策略迭代效率以及泛化能力,并且智能体数量增多时仍可保持稳定的效果,有助于将多智能体强化学习应用到更广泛的领域.

When multi-agent systems cooperate or compete,the joint information space will be enlarged and the efficiency of information extraction between agents will be reduced.In this paper,a multi-agent reinforcement learning strategy(FMAC)with filtering mechanism to filter information is adopted to enhance the ability of information communication between agents.By finding the related agents and calculating their information contribution according to the correlation,the method filters out the irrelevant agent information so as to realize the effective communication between agents in cooperative competition or mixed environment.At the same time,the centralized training decentralized execution method is adopted to solve the non-stationarity of environment.In this paper,experiments are carried out by comparing algorithms to verify that the improved algorithm improves the strategy iteration efficiency and generalization ability,and can maintain stable effects when the number of agents increases,which is conducive to the application of multi-agent reinforcement learning to a wider range of fields.