融合三支多属性决策与SAC的兵棋推演智能决策技术
Intelligent decision-making technology for wargame by integrating three-way multiple attribute decision-making and SAC
彭莉莎 1孙宇祥 2薛宇凡 2周献中3
作者信息
- 1. 南京大学工程管理学院,江苏南京 210008;浙江财经大学信息技术与人工智能学院,浙江杭州 310018
- 2. 南京大学工程管理学院,江苏南京 210008
- 3. 南京大学工程管理学院,江苏南京 210008;南京大学智能装备新技术研究中心,江苏南京 210008
- 折叠
摘要
近年来,将深度强化学习技术用于兵棋推演的智能对抗策略生成受到广泛关注.针对强化学习决策模型采样率低、训练收敛慢以及智能体博弈胜率低的问题,提出一种融合三支多属性决策(three-way multiple at-tribute decision making,TWMADM)与强化学习的智能决策技术.基于经典软表演者-批评家(soft actor-critic,SAC)算法开发兵棋智能体,利用TWMADM方法评估对方算子的威胁情况,并将该威胁评估结果以先验知识的形式引入到SAC算法中规划战术决策.在典型兵棋推演系统中开展博弈对抗实验,结果显示所提算法可有效加快训练收敛速度,提升智能体的对抗策略生成效率和博弈胜率.
Abstract
In recent years,the generation of intelligent confrontation strategies using deep reinforcement learning technology for wargaming has attracted widespread attention.Aiming at the problems of low sampling rate,slow training convergence of reinforcement learning decision model and low game winning rate of agents,an intelligent decision-making technology integrating three-way multiple attribute decision making(TWMADM)and reinforcement learning is proposed.Based on the classical soft actor-critic(SAC)algorithm,the wargaming agent is developed,and the threat situation of the opposing operator is evaluated by using TWMADM method,and the threat assessment results are introduced into the SAC algorithm in the form of prior knowledge to plan tactical decisions.A game confrontation experiment is conducted in a typical wargame system,and the results shows that the proposed algorithm can effectively speed up the training convergence,improve the efficiency of generating adversarial strategies and the game winning rate for agents.
关键词
兵棋推演/三支多属性决策/软表演者-批评家/强化学习/智能决策Key words
wargame/three-way multiple attribute decision making(TWMADM)/soft actor-critic(SAC)/reinforcement learning(RL)/intelligent decision引用本文复制引用
基金项目
国家自然科学青年基金(62306135)
教育部青年基金(23YJC630156)
江苏省青年基金(BK20230783)
南京大学技术创新基金(SC-2023-039)
出版年
2024
NETL
NSTL
万方数据