Autonomous Maneuvering Decision-Making Method for Unmanned Aerial Vehicle Based on Soft Actor-Critic Algorithm
Abstract
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.
Fields of Study & Subjects
Aerospace and Aviation Technology
UAV Applications and Optimization
Distributed Control Multi-Agent Systems
Publication details
- Journal / source
- Drones
- Publisher
- MDPI AG
- Volume
- 10
- Issue
- 1
- Pages
- 35–35
- Publication date
- 6 January 2026
- Language
- EN