Reinforcement Learning for UAV-Assisted Intelligent Transportation Systems: Solutions, Challenges, and Future Directions

This paper comprehensively explores how Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods can address diverse challenges in Unmanned Aerial Vehicle (UAV)-assisted Intelligent Transportation Systems (ITS), ranging from coverage expansion to enhancing driver assistance and ensuring communication security. To facilitate further research endeavors, the paper offers insights into selecting suitable RL models for specific scenarios. In addition, it introduces a practical seven-step methodology for modeling UAV-assisted ITS as an RL problem, providing a structured approach for researchers to follow. The paper presents various RL and DRL algorithms, such as Q-learning, Deep Q-Network, and Deep Deterministic Policy Gradient, to maximize the benefits of ITS through autonomous decision-making and learning mechanisms. In addition, the paper explores the current challenges in this field. It outlines future research directions, paving the way for advances in integrating RL/DRL techniques with UAV-assisted ITS.