Multi-attribute 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. The paper proposes an algorithm that combines the multi-attribute decision-making and reinforcement learning methods to apply to multi-agents' decision-making for wargaming AI (artificial intelligence). 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 multi-attribute decision-making method based on 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 RNM-PPO (Real Number Multi-attribute decision-making-PPO) algorithm of multi-attribute decision-making combined with reinforcement learning presented in the paper is significantly more intelligent than the pure reinforcement learning algorithm.