Multiattribute Decision-Making in Wargames Leveraging the Entropy–Weight Method in Conjunction With Deep Reinforcement Learning

With the development of society, intelligent games have gradually become a hot research field. This article proposes an algorithm that combines the multiattribute decision-making and reinforcement learning methods to apply to multiagents’ decision-making for wargaming artificial intelligence (AI).This algorithm solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training. At the same time, a multiattribute decision-making method based on the entropy–weight method was proposed to obtain the normalized weighting for each attribute that feeds into a deep reinforcement learning model. A simulation experiment confirms that the real-number multiattribute decision-making-proximal policy optimization (PPO) algorithm of multiattribute decision-making combined with reinforcement learning presented in this article is significantly more intelligent than the pure reinforcement learning algorithm.