With the rapid pace of global urbanization and rising energy demands, efficient gas leak detection is vital for public safety. This study proposes an efficient and sensitive gas leak detection method based on reinforcement learning to enhance localization speed and robustness. The approach includes critical area identification, reinforcement learning model training, and leak point localization. Simultaneously introducing noise and missing data to test the robustness of the model.