@article{Miao2025, 
author = {Peng Miao},
title = {A finite-time Q-Learning algorithm with finite-time constraints},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {10},
pages = {23380-23393},
keywords = {finite-time, Q-Learning algorithm, mountain car, parameter selection strategy},
url = {https://www.sciopen.com/article/10.3934/math.20251038},
doi = {10.3934/math.20251038},
abstract = {In this paper, a finite-time Q-Learning algorithm is designed and applied to the mountain car problem. Compared with traditional Q-Learning algorithms, our designed finite-time Q-Learning algorithm can achieve learning objectives more rapidly. It is widely recognized that the operational efficiency of the Q-Learning algorithm heavily depends on the capabilities of the underlying hardware, and computational processes often consume a considerable amount of time. To reduce the time overhead associated with Q-Learning execution, this study utilizes the theoretical framework of finite-time stability to devise a novel Q-Learning algorithm. This innovative approach has been effectively implemented to tackle challenges related to the mountain car problem. Simulation results show a significant reduction in training completion time, along with a substantial increase in the subsequent success rate of the algorithm's performance.}
}