即时策略博弈在线对抗规划方法综述

Survey on Online Adversarial Planning for Real-time Strategy Game

即时策略博弈在线规划是多智能体学习领域的挑战性问题,在博弈对抗过程中,面对不确定性威胁环境和非平稳性对手,智能体需要在有限时间内根据博弈对抗态势推理对方的行动,在巨大的状态空间和动作空间中快速做出己方行动规划,组织对抗规划。即时策略博弈平台是研究在线对抗规划问题的理想测试床。文中首先借助一个典型的即时策略博弈模型引出即时策略博弈对抗问题,并将其分类成3个层次和2种操作控制方法,从5个子方向梳理了即时策略博弈面临的5个挑战;其次从战术对抗、策略对抗和混合对抗3个角度对当前在线对抗规划方法进行了全面的综述分析;最后从对手及玩家建模、人机协同在线临机规划、基于学习的规划方法3个方面点明了未来需要研究的重点问题。

Real-time strategy game online adversarial planning is a challenging problem in the field of multi-agent learning.In the process of game confrontation,in the face of an uncertain threat environment and non-stationary opponents,the agent needs to reason about the opponent’s actions within a limited time according to the game situation,make your own action plan quickly and perform adversarial planning in the huge state space and action space.The real-time strategy game platform is an ideal testbed for studying online adversarial planning problems.This paper firstly uses a typical real-time strategy game model to elicit the real-time strategy game confrontation problems,and classifies them into three levels and two operation control methods,and sorts out the five challenges faced from five sub-directions.Secondly,the current online adversarial planning methods are comprehensively reviewed and analyzed from three perspectives of tactical adversarial planning,strategic adversarial planning and mixed adversarial planning.Finally,the key issues that need to be studied in the future are pointed out from three key aspects:opponent and player modeling,human-machine collaborative online ad hoc planning,and learning-based planning.