@inproceedings{d05ee91f4685436480ca0d8126322593,
title = "Safe Robot Navigation Using Constrained Hierarchical Reinforcement Learning",
abstract = "Safe navigation is one of the steps necessary for achieving autonomous control of robots. Among different algorithms that focus on robot navigation, Reinforcement Learning (and more specifically Deep Reinforcement Learning) has shown impressive results for controlling robots with complex and high-dimensional state representations. However, when integrating methods to comply with safety requirements by means of constraint satisfaction in flat Reinforcement Learning policies, the system performance can be affected. In this paper, we propose a constrained Hierarchical Reinforcement Learning framework with a safety layer used to modify the low-level policy to achieve a safer operation of the robot. Results obtained in simulation show that the proposed method is better at retaining performance while keeping the system in a safe region when compared to a constrained flat model.",
keywords = "Constrained Reinforcement Learning, Hierarchical Reinforcement Learning, Robot Navigation, Safety",
author = "{Schmoeller Roza}, Felippe and Hassan Rasheed and Karsten Roscher and Xiangyu Ning and Stephan G{\"u}nnemann",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022 ; Conference date: 12-12-2022 Through 14-12-2022",
year = "2022",
doi = "10.1109/ICMLA55696.2022.00123",
language = "English",
series = "Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "737--742",
editor = "Wani, {M. Arif} and Mehmed Kantardzic and Vasile Palade and Daniel Neagu and Longzhi Yang and Kit-Yan Chan",
booktitle = "Proceedings - 21st IEEE International Conference on Machine Learning and Applications, ICMLA 2022",
}