Research on UAV Path Planning Evolutionary Algorithm Based on Reinforcement Learning
Chen Xu · Theory and Practice of Science and Technology · 2025
This paper presents an investigation into the integration of optimization algorithms with learning algorithms to enhance the performance of unmanned aerial vehicles (UAVs) in path planning. Specifically, an evolutionary algorithm is employed in conjunction with the reinforcement learning DQN model to improve its efficacy in UAV path planning. The evolutionary algorithm component focuses on population initialization methods and comparative analysis of several GWO algorithms. Subsequently, the DQN model training incorporates guidance from the evolutionary algorithm, demonstrating superior performance in simulated environments compared to the original training algorithm. Finally, a discussion and analysis of optimization algorithms and reinforcement learning algorithms in the path planning domain are provided, along with several research recommendations for future DQN model training.