摘要
Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.
Suitable rescue path selection is very important to rescue lives and reduce the loss of disasters, and has been a key issue in the field of disaster response management. In this paper, we present a path selection algorithm based on Q-learning for disaster response applications. We assume that a rescue team is an agent, which is operating in a dynamic and dangerous environment and needs to find a safe and short path in the least time. We first propose a path selection model for disaster response management, and deduce that path selection based on our model is a Markov decision process. Then, we introduce Q-learning and design strategies for action selection and to avoid cyclic path. Finally, experimental results show that our algorithm can find a safe and short path in the dynamic and dangerous environment, which can provide a specific and significant reference for practical management in disaster response applications.
基金
supported by National Basic Research Program of China (973 Program) (No. 2009CB326203)
National Natural Science Foundation of China (No. 61004103)
the National Research Foundation for the Doctoral Program of Higher Education of China (No. 20100111110005)
China Postdoctoral Science Foundation (No. 20090460742)
National Engineering Research Center of Special Display Technology (No. 2008HGXJ0350)
Natural Science Foundation of Anhui Province (No. 090412058, No. 070412035)
Natural Science Foundation of Anhui Province of China (No. 11040606Q44, No. 090412058)
Specialized Research Fund for Doctoral Scholars of Hefei University of Technology (No. GDBJ2009-003, No. GDBJ2009-067)
