Back to Search
Start Over
基于分层强化学习的自动驾驶车辆掉头问题研究.
- Source :
-
Application Research of Computers / Jisuanji Yingyong Yanjiu . Oct2022, Vol. 39 Issue 10, p3008-3045. 6p. - Publication Year :
- 2022
-
Abstract
- The U-turn task is one of the contents of autonomous driving research, and most of the solutions under the standard roads in cities cannot be implemented on non-standard roads. Aiming at solving this problem, this paper establishes a vehicle U-turn dynamical model and designs a multi-scale convolutional neural network to extract feature maps as the input of the agent. In addition, for the sparse reward problem in the U-turn task, this paper proposes a hierarchical proximal policy optimization algorithm that combines hierarchical reinforcement learning and proximal policy optimization algorithm. In experiments with simple and complex scenarios, this algorithm learns policies faster and has a higher success rate of U-turn compared to other algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- Chinese
- ISSN :
- 10013695
- Volume :
- 39
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- Application Research of Computers / Jisuanji Yingyong Yanjiu
- Publication Type :
- Academic Journal
- Accession number :
- 159586974
- Full Text :
- https://doi.org/10.19734/j.issn.1001-3695.2022.03.0127