An Intelligent Maneuver Decision-Making Approach for Air Combat Based on Deep Reinforcement Learning and Transformer Networks

Abstract

The traditional maneuver decision-making approaches are highly dependent on accurate and complete situation information, and their decision-making quality becomes poor when opponent information is occasionally missing in complex electromagnetic environments. In order to solve this problem, an autonomous maneuver decision-making approach is developed based on deep reinforcement learning (DRL) architecture. Meanwhile, a Transformer network is integrated into the actor and critic networks, which can find the potential dependency relationships among the time series trajectory data. By using these relationships, the information loss is partially compensated, which leads to maneuvering decisions being more accurate. The issues of limited experience samples, low sampling efficiency, and poor stability in the agent training state appear when the Transformer network is introduced into DRL. To address these issues, the measures of designing an effective decision-making reward, a prioritized sampling method, and a dynamic learning rate adjustment mechanism are proposed. Numerous simulation results show that the proposed approach outperforms the traditional DRL algorithms, with a higher win rate in the case of opponent information loss.