Please use this identifier to cite or link to this item: https://doi.org/10.1109/ACCESS.2020.3011438
Title: A Survey on Visual Navigation for Artificial Agents with Deep Reinforcement Learning
Authors: Zeng, F.
Wang, C.
Ge, S.S. 
Keywords: artificial agents
deep reinforcement learning
Survey
visual navigation
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Zeng, F., Wang, C., Ge, S.S. (2020). A Survey on Visual Navigation for Artificial Agents with Deep Reinforcement Learning. IEEE Access 8 : 135426-135442. ScholarBank@NUS Repository. https://doi.org/10.1109/ACCESS.2020.3011438
Abstract: Visual navigation (vNavigation) is a key and fundamental technology for artificial agents' interaction with the environment to achieve advanced behaviors. Visual navigation for artificial agents with deep reinforcement learning (DRL) is a new research hotspot in artificial intelligence and robotics that incorporates the decision making of DRL into visual navigation. Visual navigation via DRL, an end-to-end method, directly receives the high-dimensional images and generates an optimal navigation policy. In this paper, we first present an overview on reinforcement learning (RL), deep learning (DL) and deep reinforcement learning (DRL). Then, we systematically describe five main categories of visual DRL navigation: direct DRL vNavigation, hierarchical DRL vNavigation, multi-task DRL vNavigation, memory-inference DRL vNavigation and vision-language DRL vNavigation. These visual DRL navigation algorithms are reviewed in detail. Finally, we discuss the challenges and some possible opportunities to visual DRL navigation for artificial agents. @ 2013 IEEE.
Source Title: IEEE Access
URI: https://scholarbank.nus.edu.sg/handle/10635/198956
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.3011438
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