@article{Zhu2021, 
author = {Kai Zhu and Tao Zhang},
title = {Deep Reinforcement Learning Based Mobile Robot Navigation: A Review},
year = {2021},
journal = {Tsinghua Science and Technology},
volume = {26},
number = {5},
pages = {674-691},
keywords = {mobile robot navigation, obstacle avoidance, deep reinforcement learning},
url = {https://www.sciopen.com/article/10.26599/TST.2021.9010012},
doi = {10.26599/TST.2021.9010012},
abstract = {Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.}
}