Application of Reinforcement Learning to UAV Decision Making and Path Planning

The aim of this study is to systematically explore the framework and potential of reinforcement learning for UAV decision-making and path planning. The article firstly analyses the core advantages of reinforcement learning over traditional planning methods. its ability to cope with environmental uncertainty, achieve multi-objective optimisation, and complete end-to-end decision-making. Then, by focusing on three typical application scenarios, namely autonomous navigation and obstacle avoidance, multi-UAV collaboration, and resource-constrained planning, the paper analyses the technical path and effectiveness of reinforcement learning in solving key problems in specific domains. Despite the challenges of sample efficiency, security, and interpretability, cutting-edge developments such as simulation-to-reality migration and constrained reinforcement learning are continuing to drive the technology towards practicality. The systematic discussion in this study aims to provide theoretical references for cross-disciplinary research on reinforcement learning and robotics, and to lay an important technical foundation for building the next-generation intelligent, robust and adaptive unmanned aerial systems.