The breakthrough and progress of intelligent gaming technology with deep reinforcement learning as the core in the field of games provide a method reference for the research of agents Armoire in sea-air wargames.The architecture design of the agent is the primary core key problem that needs to be solved, and a good architecture can reduce the complexity and difficulty of training and accelerate the convergence of policies.A stochastic game model of sea-air cross-domain cooperative decision-making has been proposed, and its corresponding equilibrium solution concepts have been analyzed.Based on the analysis of typical agent frameworks, aiming at the decision-making gaming process of sea-air wargames, and then an agent bi-level Brake Cam architecture based on multi-Agent hierarchical reinforcement learning is proposed, which can effectively solve the problems of collaboration and dimensional disaster.The key technologies are analyzed from four aspects: force coordination, agent network design, adversary modeling and training mechanism.
Hoping to provide architectural guidance for the subsequent design and implementation of sea-air wargaming agents.