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$$Research on fusion algorithm of multi-attribute decision making and reinforcement learning based on intuitionistic fuzzy number in wargame environment$$

Intelligent games have seen an increasing interest within the research community on artificial intelligence. The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that joined their effect on wargaming, it 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, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, yielding data on the conflict between red and blue sides. We calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations. And then we determine the threat posed by each opponent’s game agents . Using the red side reinforcement learning reward function, the AC framework is trained on the reward function, and an algorithm combining multi-attribute decision making with reinforcement learning is obtained. A simulation experiment confirms that the algorithm of multi-attribute decision making combined with reinforcement learning presented in this paper is significantly more intelligent than the pure reinforcement learning algorithm. By resolving the shortcomings of the agent’s neural network, coupled with sparse rewards in large-map combat games, this robust algorithm effectively reduces the difficulties of convergence. It is also the first time in this field that an algorithm design for intelligent wargaming combines multi-attribute decision making with reinforcement learning. Finally, another novelty of this research is the interdisciplinary, like designing intelligent wargames and improving reinforcement learning algorithms.

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