The invention discloses an intelligent group
area coverage control-oriented multilayer game
reinforcement learning method, which comprises the following steps of: mapping each agent in an intelligent group into a
game player, and constructing an income evaluation mechanism in combination with a task target of the agent to form a group game model; constructing a
perception model and a communication model based on physical capability constraints of the
intelligent agent, designing evaluation indexes of coverage efficiency,
operation safety,
energy consumption efficiency and communication
collaboration, and integrating the evaluation indexes into a vector revenue function of the
intelligent agent; a TD3 model is adopted as a
game player role of an
intelligent agent, and association between
reinforcement learning and group game is established; and constructing a multi-layer semi-distributed game
reinforcement learning framework, carrying out environment interaction, and solving an intelligent group
area coverage control strategy meeting Pareto-Nash equilibrium. According to the method, the balance of individual and collective benefits is realized, the calculation efficiency is improved through a semi-distributed iterative
algorithm, and the method is suitable for solving requirements of a large-scale
complex system.