Multi-Attention Meets Pareto Optimization: A Reinforcement Learning Method for Adaptive UAV Formation Control
Year: 2025
Type: article
Abstract: Autonomous multi-UAV formation control in cluttered urban environments remains challenging due to partial observability, dense and dynamic obstacles, and conflicting objectives (task efficiency, energy use, and safety). Yet many MARL-based approaches still collapse vector-valued objectives into a single hand-tuned reward and lack selective information fusion, leading to brittle trade-offs and poor scalability in urban clutter. We introduce a model-agnostic MARL framework—instantiated on MADDPG for concreteness—that augments a CTDE backbone with three lightweight attention modules (self, inter-agent, and entity) for selective information fusion, and a Pareto optimization module that maintains a compact archive of non-dominated policies to adaptively guide objective tradeoffs using simple, interpretable rewards rather than fragile weightings. On city-scale navigation tasks, the approach improves final team success by 13–27 percentage points for N=2–5 while simultaneously reducing collisions, tightening formation, and lowering control effort. These gains require no algorithmspecific tuning and scale smoothly beyond two agents, underscoring a stronger safety–efficiency trade-off and robust applicability in cluttered, partially observable settings.
Source: Drones
Authors: Li Zheng, Junjie Zeng, Long Qin, Rusheng Ju
Institution: National University of Defense Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4417124011