V-Pilot: A Velocity Vector Control Agent for Fixed-Wing UAVs from Imperfect Demonstrations

Abstract

This paper addresses the challenge of Velocity Vector Control (VVC) for fixed-wing UAVs using Reinforcement Learning (RL) in the presence of imperfect demonstrations. The multi-objective and long-horizon nature of VVC introduces significant spatial and temporal complexities, complicating RL's exploration. While demonstration-based RL methods can help mitigate exploration challenges, their effectiveness is often limited by the quality of the provided demonstrations. To tackle this, we propose V-Pilot, a novel approach that integrates: (1) a controller equipped with a control law model to reduce action oscillation, thus alleviating temporal exploration issues, and (2) a VVC-specific training workflow for iterative policy refinement and demonstration quality improvement. This framework is designed to enhance the performance of demonstration-based RL under imperfect demonstrations. We evaluate V-Pilot on the fixed-wing UAV RL environment, VVCGym. Experimental results demonstrate that V-Pilot outperforms PID and Behavioral Cloning across multiple performance metrics.