Sangho Yeo, Seungjun Lee, Sangyoon Oh

KIPS Transactions on Software and Data Engineering, Vol. 10, No. 7, pp. 263-270, Jul. 2021
https://doi.org/10.3745/KTSDE.2021.10.7.263,   PDF Download:
Keywords: Reinforcement Learning Environment, Disaster Response Simulation, Dimension Reduction Method, PCA
Abstract

Reinforcement learning(RL) is the method to find an optimal policy through training. and it is one of popular methods for solving lifesaving and disaster response problems effectively. However, the conventional reinforcement learning method for disaster response utilizes either simple environment such as. grid and graph or a self-developed environment that are hard to verify the practical effectiveness. In this paper, we propose the design of a disaster response RL environment which utilizes the detailed property information of the disaster simulation in order to utilize the reinforcement learning method in the real world. For the RL environment, we design and build the reinforcement learning communication as well as the interface between the RL agent and the disaster simulation. Also, we apply the dimension reduction method for converting non-image feature vectors into image format which is effectively utilized with convolution layer to utilize the high-dimensional and detailed property of the disaster simulation. To verify the effectiveness of our proposed method, we conducted empirical evaluations and it shows that our proposed method outperformed conventional methods in the building fire damage.

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