基于深度强化学习与自学习的多无人机近距空战机动策略生成算法

In order to solve the problem of multi-UAV close-range air combat maneuvering decision-making, a multi- UAV close-range air combat maneuvering strategy generation algorithm based on parameter sharing Q network and neural fictitious self-play is proposed. Firstly, a hybrid Markov game model suitable for different UAV formation sizes and a reinforcement learning framework for generating maneuvering decision strategies of multi-UAV are designed-parameter sharing Q network, and the state space is compressed through the autoencoder to improve the efficiency of strategy learning. Then, using the neural fictitious self-play makes the maneuver strategy converge to the Nash equilibrium strategy. Finally, simulation experiments are carried out on the parameter selection of the autoencoder, the training process of the strategy generation algorithm, and the rationality and portability of the maneuver strategy. The simulation results show that the autoencoder is introduced can effectively improve the efficiency of strategy learning, and the multi-UAV short-range air combat maneuver strategy generated by this algorithm is reasonable and good portability.