Decision-making method for air combat maneuver based on explainable reinforcement learning

Intelligent air combat is the trend of air combat in the future，and deep reinforcement learning is an impor- tant technical way to realize intelligent decision-making in air combat. However，due to the characteristic of“black box model”，deep reinforcement learning has the shortcomings such as difficulty in explaining strategies，understanding in- tentions，and trusting decisions，which brings challenges to the application of deep reinforcement learning in intelligent air combat. To solve these problems，an intelligent air combat maneuver decision-making method is proposed based on explainable reinforcement learning. Firstly，based on the strategy-level explanation method and dynamic Bayesian network，an interpretability model and the maneuvering intention recognition model are constructed. Secondly，through calculation of the importance of the decision and the probability of maneuvering intention，the intention-level of the Unmanned Aerial Vehicle（UAV）maneuver decision-making process can be explained. Finally，based on the in- tent interpretation results，the reward function and training strategy of the deep reinforcement learning algorithm are modified，and the effectiveness of the proposed method is verified by simulation and comparative analysis. The pro- posed method can obtain air combat maneuver strategies with excellent effectiveness，strong reliability，and high credibility. © 2024 Chinese Society of Astronautics. All rights reserved.