Exploring the Application of Reinforcement Learning in the Path Planning Algorithm of UAVs

UAVs are widely used in areas such as monitoring, delivery, and disaster rescue due to their ability to work in harsh environments. Classic UAV path planning algorithms rely on pre-known accurate environment maps. How can a UAV quickly learn appropriate ways under unknown real conditions? This poses a crucial research problem for path planning of unmanned aircraft vehicles in reality. Autonomous autonomous path planning technology has become more important because of the ever-increasing application occasions of unmanned aerial vehicles. As opposed to other navigation technologies, Reinforcement Learning(RL) provides drones with learning skills to master how to navigate using only interactions with an area, not maps or 3D models of that region. Therefore, this paper first makes a survey and analysis of Reinforcement Learning in UAV path planning and then summarizes key advantages and present drawbacks of typical RL methods for navigating autonomous agents between waypoints, ranging from Q-learning techniques up to modern methods such as A3C and HRL. At last, it concludes smooth-policy-achieved advantage-function-algorithms are proper for constructing good smooth motion plans in continuous-state spaces, where multi-layer hierarchy architecture will also provide reasonable options but mainly at larger scale instances, thereby directing next-stage research activities towards the optimization of the automated movement system in the unmanned plane in favor of a broader development into more wise flight control agents.