Multi-objective vehicle path planning based on DQN

Qingyu Huo

doi:10.1117/12.2640707

13 October 2022 Multi-objective vehicle path planning based on DQN

Qingyu Huo

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871E (2022) https://doi.org/10.1117/12.2640707
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

In the path planning problem based on geographic information system, distance and resource overhead often conflict with each other. For example, the shortest path may not be the most time-saving or convenient one. So, a Multi-objective vehicle path planning method is needed. Therefore, this paper proposes a vehicle path planning algorithm based on improved deep reinforcement learning, which not only solves the problem of single optimization objective restriction, but also reduce the chance of falling into local optimum in traditional path planning algorithm. In this paper, DQN network structure is selected, and neural network is used to store action value function instead of Q table. In the training process, prioritized experience replay strategy is used to accelerate the convergence. The simulation experiment and analysis of the proposed model show that the improved DQN algorithm model can effectively find the path with the optimal value, making the path planning more rational and intelligent.

Citation Download Citation

Qingyu Huo "Multi-objective vehicle path planning based on DQN", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871E (13 October 2022); https://doi.org/10.1117/12.2640707

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