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