Intelligent decision-making technology for wargame by integrating three-way multiple attribute decision-making and SAC

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. © 2024 Chinese Institute of Electronics. All rights reserved.