Aiming at addressing the problem of manoeuvring decision‐making in UAV air combat, this study establishes a one‐to‐one air combat model, defines missile attack areas, and uses the non‐deterministic policy Soft‐Actor‐Critic (SAC) algorithm in deep reinforcement learning to construct a decision model to realize the manoeuvring process. At the same time, the complexity of the proposed algorithm is calculated, and the stability of the closed‐loop system of air combat decision‐making controlled by neural network is analysed by the Lyapunov function. This study defines the UAV air combat process as a gaming process and proposes a Parallel Self‐Play training SAC algorithm (PSP‐SAC) to improve the generalisation performance of UAV control decisions. Simulation results have shown that the proposed algorithm can realize sample sharing and policy sharing in multiple combat environments and can significantly improve the generalisation ability of the model compared to independent training.

In this paper, an intelligent algorithm integrating model predictive control and Standoff algorithm is proposed to solve trajectory planning that UAVs may face while tracking a moving target cooperatively in a complex three-dimensional environment. A fusion model using model predictive control and Standoff algorithm is thus constructed to ensure trajectory planning and formation maintenance, maximizing UAV sensors’ detection range while minimizing target loss probability. Meanwhile, with this model, a fully connected communication topology is used to complete the UAV communication, multi-UAV formation can be reconfigured and planned at the minimum cost, keeping off deficiency in avoiding real-time obstacles facing the Standoff algorithm. Simulation validation suggests that the fusion algorithm proves to be more capable of maintaining UAVs in stable formation and detecting the target, compared with the model predictive control algorithm alone, in the process of tracking the moving target in a complex 3D environment.

Research on UAV (unmanned aerial vehicle) path planning and obstacle avoidance control based on DRL (deep reinforcement learning) still faces limitations, as previous studies primarily utilized current perceptual inputs while neglecting the continuity of flight processes, resulting in low early-stage learning efficiency. To address these issues, this paper integrates DRL with the Transformer architecture to propose the GTrXL-SAC (gated Transformer-XL soft actor critic) algorithm. The algorithm performs positional embedding on multimodal data combining visual and sensor information. Leveraging the self-attention mechanism of GTrXL, it effectively focuses on different segments of multimodal data for encoding while capturing sequential relationships, significantly improving obstacle recognition accuracy and enhancing both learning efficiency and sample efficiency. Additionally, the algorithm capitalizes on GTrXL’s memory characteristics to generate current drone control decisions through the combined analysis of historical experiences and present states, effectively mitigating long-term dependency issues. Experimental results in the AirSim drone simulation environment demonstrate that compared to PPO and SAC algorithms, GTrXL-SAC achieves more precise policy exploration and optimization, enabling superior control of drone velocity and attitude for stabilized flight while accelerating convergence speed by nearly 20%.

The demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial. Unmanned aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy; however, limits their combat capabilities, which proves to be very challenging. To meet the challenge, this article proposes an autonomous manoeuvre decision model using an expert actor‐based soft actor critic algorithm that reconstructs empirical replay buffer with expert experience. Specifically, the algorithm uses a small amount of expert experience to increase the diversity of the samples, which can largely improve the exploration and utilisation efficiency of deep reinforcement learning. And to simulate the complex battlefield environment, a one‐to‐one air combat model is established and the concept of missile's attack region is introduced. The model enables the one‐to‐one air combat to be simulated under different initial battlefield situations. Simulation results show that the expert actor‐based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster, and converge more quickly, compared with the soft actor critic algorithm.