Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169074
Title: Toward human-in-the-loop AI: enhancing deep reinforcement learning via real-time human guidance for autonomous driving
Authors: Wu, Jingda
Huang, Zhiyu
Hu, Zhongxu
Lv, Chen
Keywords: Engineering::Mechanical engineering
Issue Date: 2023
Source: Wu, J., Huang, Z., Hu, Z. & Lv, C. (2023). Toward human-in-the-loop AI: enhancing deep reinforcement learning via real-time human guidance for autonomous driving. Engineering, 21, 75-91. https://dx.doi.org/10.1016/j.eng.2022.05.017
Project: NAP-SUG 
W1925d0046 
Journal: Engineering 
Abstract: Due to its limited intelligence and abilities, machine learning is currently unable to handle various situations thus cannot completely replace humans in real-world applications. Because humans exhibit robustness and adaptability in complex scenarios, it is crucial to introduce humans into the training loop of artificial intelligence (AI), leveraging human intelligence to further advance machine learning algorithms. In this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developed for policy training in an end-to-end autonomous driving case. With our newly designed mechanism for control transfer between humans and automation, humans are able to intervene and correct the agent's unreasonable actions in real time when necessary during the model training process. Based on this human-in-the-loop guidance mechanism, an improved actor-critic architecture with modified policy and value networks is developed. The fast convergence of the proposed Hug-DRL allows real-time human guidance actions to be fused into the agent's training loop, further improving the efficiency and performance of DRL. The developed method is validated by human-in-the-loop experiments with 40 subjects and compared with other state-of-the-art learning approaches. The results suggest that the proposed method can effectively enhance the training efficiency and performance of the DRL algorithm under human guidance without imposing specific requirements on participants’ expertise or experience.
URI: https://hdl.handle.net/10356/169074
ISSN: 2095-8099
DOI: 10.1016/j.eng.2022.05.017
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2022 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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