Reinforcement Learning has been used on path planning for a long time, which is thought to be very effective, especially the Value Iteration Networks (VIN) with strong generalization ability. In this paper, we analyze the path planning of VIN and propose a method that can effectively find vulnerable points in VIN. We build a 2D navigation task to test our method. The experiment for interfering VIN is conducted for the first time. The experimental results show that our method has good performance on finding vulnerabilities and could automatically adding obstacles to obstruct VIN path planning.
CITATION STYLE
Liu, J., Niu, W., Liu, J., Zhao, J., Chen, T., Yang, Y., … Han, L. (2018). A method to effectively detect vulnerabilities on path planning of VIN. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10631 LNCS, pp. 374–384). Springer Verlag. https://doi.org/10.1007/978-3-319-89500-0_33
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