基于RACE-MFT算法的多智能体战略状态融合模型研究OA

In military simulation strategies,the execution of combat tasks often relies on efficient teamwork,and multi-agent rein-forcement learning methods have been widely introduced to meet this demand.However,in practical applications,due to insufficient ob-servation and construction of the environmental state by multi-agent systems,the information they obtain is relatively limited,making it difficult to meet the requirements of battlefield perception in complex military tasks.A method for improving multi-modal information fu-sion technology based on RACE(RACE-MFT)is proposed to address this issue.This method integrates attribute,text,and image informa-tion to construct a richer and more comprehensive representation of the agent's state,thereby increasing the state dimension and enhanc-ing the agent's information perception ability,enabling it to make better decisions.The experiment was conducted in the real-time strate-gy game StarCraft II and a self built"Battle for Key Tasks"environment.The results showed that in StarCraft II,when using RACE-MFT agents to compete against the game's built-in AI,the win rate increased by 3%.In the confrontation between the improved algorithm and the original algorithm,the winning rate remains stable at 80%.In the environment of"competing for important places",compared with other single module improvements,the convergence reward of RACE-MFT reaches the maximum.These all confirm the effectiveness of RACE-MFT in handling multi-agent team collaboration tasks.