Safe UAV Control Against Wind Disturbances via Demonstration-Guided Reinforcement Learning

Unmanned Aerial Vehicle (UAV) operating in complex environments require guaranteed safety mechanisms while maintaining high performance. This study addresses the challenge of ensuring strict flight safety during policy execution by implementing a Control Barrier Function (CBF) as a real-time action filter, thereby providing a rigorous, formal guarantee. The methodology integrates the primary Proximal Policy Optimization (PPO) algorithm with a Demonstration-Guided Reinforcement Learning (DGRL), which leverages Proportional–Integral–Derivative (PID) expert trajectories to significantly accelerate learning convergence and enhance sample efficiency. Comprehensive results confirm the efficacy of the hybrid architecture, demonstrating a significant reduction in constraint violations and proving the framework’s ability to substantially accelerate training compared to PPO. In conclusion, the proposed methodology successfully unifies formal safety guarantees with efficient, adaptive reinforcement learning, making it highly suitable for safety-critical autonomous systems.