The invention discloses a heterogeneous
spacecraft cluster target
allocation method based on a multi-agent
reinforcement learning algorithm, and mainly relates to a heterogeneous
weapon system model design and a hierarchical
hybrid collaborative decision framework design based on multi-agent
reinforcement learning. According to the method, the problem of heterogeneous target allocation of the
spacecraft cluster in the technical field of
spacecraft cooperative control is solved. The spacecraft cluster heterogeneous target
allocation method based on multi-agent
reinforcement learning comprises the following steps: 1, establishing a heterogeneous weapon target allocation model for a spacecraft cluster heterogeneous target allocation problem; 2, constructing a'global-local-
monomer 'hierarchical
decision model based on a multi-agent depth deterministic policy gradient (MADDPG)
algorithm framework, wherein a global task allocation layer agent adopts centralized training to learn a heterogeneous weapon task allocation and cluster hierarchical strategy; and intelligent agents of the local
collaboration layer and the single execution layer adopt distributed training and local observation to carry out collaborative
decision making and
target distribution. And 3, designing a composite reward function fusing the target value, the
intelligent agent capability matching degree and multiple constraints to guide the
intelligent agent to maximize the overall task income and generate an optimal allocation strategy on the premise of meeting the complex constraints, thereby effectively improving the heterogeneous target allocation efficiency under the spacecraft large-scale cluster. The method can be applied to the
aerospace field and in-
orbit autonomous decision-making scenes.