Unmanned aerial vehicle continuous maneuver control method based on distributed reinforcement learning

The invention relates to the technical field of machine learning, in particular to an unmanned aerial vehicle continuous maneuver control method based on distributed reinforcement learning. The method comprises the steps of constructing a simulation training environment based on unmanned aerial vehicle kinetic parameters; interacting the simulation training environment with a reinforcement learning training system, wherein the reinforcement learning training system is used for carrying out iterative updating on an unmanned aerial vehicle continuous maneuver control strategy neural network, including receiving information of a data experience pool, generating a control strategy through a training algorithm, and controlling an unmanned aerial vehicle to act according to the control strategy, and the data experience pool stores environment information generated by the simulation training environment and unmanned aerial vehicle state information; and sampling the sample data of different random parameters to obta