计算机科学
强化学习
人工智能
机器学习
马尔可夫决策过程
作者
Huanjie Wang,Hongbo Gao,Shihua Yuan,Hongfei Zhao,Kelong Wang,Xiulai Wang,Keqiang Li,Deyi Li
出处
期刊:IEEE Transactions on Vehicular Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-07-21
卷期号:70 (9): 8707-8719
被引量:1
标识
DOI:10.1109/tvt.2021.3098321
摘要
This paper presents a latent space reinforcement learning method for interpretable decision-making of autonomous vehicles at highway on-ramps. This method is based on the latent model and the combination model of the hidden Markov model and Gaussian mixture regression (HMM-GMR). It is difficult for the traditional decision-making method to understand the environment because its input is high-dimensional and lacks an understanding of the task. By utilizing the HMM-GMR model, we can obtain the interpretable state providing semantic information and environment understanding. A framework is proposed to unify representation learning with the deep reinforcement learning (DRL) approach, in which the latent model is used to reduce the dimension of interpretable state by extracting underlying task-relevant information. Experimental results are presented and the results show the right balance between driving safety and efficiency in the challenging scenarios of highway on-ramps merging.
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