Abstract
Path planning research can effectively solve the problem of finding free parking space in multi-storey parking lots. This paper takes advantage of the decision ability of reinforcement learning and the perception ability of deep learning to improve the algorithm based on traditional DQN. On the one hand, Q value is updated with qualification trace; On the other hand, different loss functions are set for the main network and the target network, and the two are combined to improve the accuracy of path planning. The experimental results show that the improved DQN model can accomplish the path planning task of multi-storey parking lot more accurately and efficiently.
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Acknowledgement
This paper is supported by the National Natural Science Foundation of China (62073231, 61902272, 62176175, 61876217, 61902271), National Research Project (2020YFC2006602), Opening Topic Fund of Big Data Intelligent Engineering Laboratory of Jiangsu Province (SDGC2157), Provincial Key Laboratory for Computer Information Processing Technology, Soochow University (KJS2166).
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Hu, Z. et al. (2022). Study on Path Planning of Multi-storey Parking Lot Based on Combined Loss Function. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_20
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