The application provides a communication interference strategy generation method based on
reinforcement learning, comprising the following steps: step one, constructing a
system model of a communication party and a communication interference party; step two, based on the
system model of the communication party and the communication interference party constructed in step one, learning an anti-interference scheme of the communication party by using a win-or-learn policy
hill climbing algorithm, and designing a corresponding interference
decision model; step three, using the interference
decision model obtained in step two, learning an anti-interference strategy of a communication target and implementing interference according to a
decision process of 'observation-adjustment-decision-action'. The application comprehensively considers interference basic principles and behavior changes of the communication target, combines a jam-to-
noise ratio and anti-interference behaviors such as frequency change and transmission power increase of the communication target after being interfered, and uses the two as measurement indexes of interference effects, so that the purpose of real-time and rapid interference is achieved.