Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning

强化学习 计算机科学 人工智能 领域(数学) 趋同(经济学) 机器学习 功能(生物学) 人工神经网络 模糊逻辑 智能代理 数学 经济增长 进化生物学 生物 经济 纯数学
作者
Sun Yan,Bo Yuan,Yufan Xue,Jiawei Zhou,Xiaoyu Zhang,Xianzhong Zhou
出处
期刊:Cornell University - arXiv
标识
DOI:10.48550/arxiv.2109.02354
摘要

Researchers are increasingly focusing on intelligent games as a hot research area.The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined 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, and obtained data on red and blue conflict.Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations. Then determine the threat posed by each opponent's chess pieces.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.Attempt interdisciplinary cross-innovation in the academic field, like designing intelligent wargames and improving reinforcement learning algorithms.
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