ASM2:Multi-agent multi-opponent game algorithm for joint sea-air scenarios
WANG Yi-song
ZHAO Ming-hui
ZHANG Xue-bo
Abstract：In the intricate air-sea joint intelligent game environment,the situational information of the game environ-ment is high-dimensional and undergoes dynamic changes.This presents a significant challenge for achieving collaborative decision-making among heterogeneous combat units.Moreover,many of the existing algorithms grapple with issues of dimensionality explosion and suboptimal generalization.Addressing the challenge of facilitating collaborative decision-making through limited situational information becomes imperative.To tackle this,this paper introduces a formalized modeling approach for the air-sea joint intelligent game.This approach can holistically and effectively characterize the situational information,command and control heterogeneous combat units,and steer the algorithm training direction.Fur-thermore,we propose the ASM2(air-sea multi-opponent multi-agent proximal policy optimization)algorithm for air-sea joint gaming.Rooted in the multi-agent proximal policy optimization(MAPPO)distributed multi-agent gaming algorithm,ASM2 incorporates a multi-opponent multi-agent training framework embedded with the Elo scoring system,enhancing the model's generalization capabilities.Validation tests on a wargame simulation platform indicate that the model,once trained with our proposed algorithm,can adeptly handle various expert opponent strategies.It showcases commendable feasibility and generalization prowess,paving the way for bolstering the combat capabilities of future complex unmanned equipment.
Keywords：intelligent confrontation of unmanned systemswargameair-sea joint operationsintelligent controlmulti-agent reinforcement learning
Publication Date：2025-07-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 1275-1284 )