Actor-Critic框架下的多智能体决策方法及其在兵棋上的应用

将人工智能应用于兵棋推演的智能战术兵棋正逐年发展,基于Actor-Critic框架的决策方法可以实现智能战术兵棋的战术行动动态决策.但若Critic网络只对单算子进行评价,多算子之间的网络没有协同,本方算子之间各自行动决策会不够智能.针对上述方法的不足,提出了一种基于强化学习并结合规则的多智能体决策方法,以提升兵棋推演...

Multi-agent decision-making method based on Actor-Critic framework and its application in wargame

The intelligent tactical wargame which applies artificial intelligence to wargame deduction is developed year by year.The decision-making method based on Actor-Critic framework can realize the dynamic decision-making of tactical action of intelligent tactical wargame.However,if the Critic network only evaluates the single agent,and there is no cooperation among multiple agents,the decision-making of each agent will not be intelligent enough.In order to improve the intelligence level of wargame deduction,a multi-agent decision-making method based on reinforcement learning and rules is proposed.The decision analysis of the multi-agent action decision by using reinforcement learning is focuses,and combining with the production rules to plan tactical decision.An action decision model based on Actor-Critic framework for multi-agent distributed execution training is constructed.Compared with the closed action decision-making learning method in which each operator does not communicate with each other,the proposed distributed execution and centralized training method is more advantageous and effective.