Autonomous Maneuvering Decision-Making Method for Unmanned Aerial Vehicle Based on Soft Actor-Critic Algorithm

Focusing on continuous action space methods for autonomous maneuvering decision making in 1v1 unmanned aerial vehicle scenarios, this paper first establishes a UAV kinematic model and a decision-making framework under the Markov Decision Process. Second, a continuous control strategy based on the Soft Actor-Critic (SAC) reinforcement learning algorithm is developed to generate precise maneuvering commands. Then, a multi-dimensional situation-coupled reward function is designed, introducing a Health Point (HP) metric to assess situational advantages and simulate cumulative effects quantitatively. Finally, extensive simulations in a custom Gym environment validate the effectiveness of the proposed method and its robustness under both ideal and noisy observation conditions.