Cooperative Multi-UAV Jamming in 3-D Uncertain Environments Using Multi-Agent Reinforcement Learning

The rapid development of drone technology has spurred significant interest in multi-UAV collaborative systems, particularly for complex tasks like cooperative target jamming. However, realizing their full potential is hindered by significant challenges, primarily stemming from uncertain three-dimensional (3-D) target positions and operational time constraints. These factors complicate crucial aspects like path planning and efficient task allocation, ultimately jeopardizing jamming mission success. Furthermore, the specific complexities introduced by uncertain 3-D target positions are often overlooked in existing cooperative jamming strategies. To address these issues, we propose cooperative multi-agent jamming techniques using reinforcement learning (RL) to maximize interference effectiveness against designated targets under target position uncertainty. Our methodology is based on a task framework that unifies the models of target position uncertainty, 3-D probabilistic perception for high-fidelity UAV sensing, and directional antenna interference to achieve optimal jamming. Within this framework, we formalize the task as a Markov Decision Process (MDP) and employ reinforcement learning to optimize collaborative jamming policies under target positions uncertainty. The proposed RL algorithm, by utilizing both individual and collaborator rewards, adaptively balances exploration and exploitation across different mission stages. This balance is achieved by adjusting the amplitude of noise used for action selection. We conducted simulation experiments with various UAV, target and no-fly zone configurations to validate the effectiveness of our proposed method, demonstrating its scalability and strong joint task performance in achieving jamming objectives.