Disaster-Aware Path Planning Based on Reinforcement Learning for Postearthquake Emergency Response

After earthquake disasters, ensuring that emergency rescue operations reach affected areas as quickly as possible, with the aim of maximizing the rescue of lives and property and minimizing secondary disaster losses, is the primary task of post-earthquake emergency response. Remote sensing technology, characterized by its large-scale coverage, non-contact nature, and rapid response capabilities, provides valuable information for disaster area assessment and post-earthquake emergency response path planning. However, existing post-disaster emergency path planning studies often fail to utilize this rich information. Traditional path planning algorithms insufficiently consider the disaster situation, hindering the efficient utilization of rescue forces and resources. To address these challenges, this study proposes a disaster-aware path planning method based on reinforcement learning for post-earthquake emergency response (P2DARL). The P2DARL method utilizes real disaster information provided by high-resolution remote sensing images and models it in a reinforcement learning environment. This allows the intelligent agent to learn optimal post-earthquake emergency response path planning strategies through continuous trial-and-error interactions with the environment. This approach facilitates the optimal allocation of rescue resources and enhances rescue efficiency. Experiments using real earthquake disaster images demonstrate that the P2DARL method effectively plans paths that cover more affected centers in mid-short distance scenarios, maximizing rescue efficiency and significantly reducing casualties and economic losses resulting from earthquake disasters.