Learning-Guided Symbolic Solver Selection for Dynamic Multi-UAV Missions in Simulation

Abstract

: Multi-unmanned Aerial Vehicle (multi-UAV) missions require task assignment strategies that adapt to dynamic urgency, threats, and resource constraints. Fixed heuristics lack flexibility, while end-to-end learned policies often omit explicit safety and verification mechanisms. This paper proposes a learning-guided neuro-symbolic orchestration framework in which reinforcement learning operates at a meta level to select among heterogeneous symbolic solvers within a closed simulation loop. Candidate assignments are regulated through a dual-layer verification mechanism comprising a feasibility filter and an episode-level simulator-based evaluator. The meta-decision layer is trained using a Double Deep Q-Network (Double DQN) with rewards reflecting completion, expiration, battery consumption, and threat exposure. Experiments across diverse scenarios demonstrate the effectiveness of adaptive solver selection and improved safety–efficiency trade-offs compared to fixed baselines.