Toward human-in-the-loop AI: enhancing deep reinforcement learning via real-time human guidance for autonomous driving
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 train...
Saved in:
Main Authors: | Wu, Jingda, Huang, Zhiyu, Hu, Zhongxu, Lv, Chen |
---|---|
Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Article |
Language: | English |
Published: |
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/169074 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Prioritized experience-based reinforcement learning with human guidance for autonomous driving
by: Wu, Jingda, et al.
Published: (2024) -
Uncertainty-aware model-based reinforcement learning: methodology and application in autonomous driving
by: Wu, Jingda, et al.
Published: (2024) -
Fear-neuro-inspired reinforcement learning for safe autonomous driving
by: He, Xiangkun, et al.
Published: (2024) -
Deep reinforcement learning for autonomous cyber operation
by: Yong, Hou Zhong
Published: (2024) -
Autonomous agents in snake game via deep reinforcement learning
by: Wei, Zhepei, et al.
Published: (2019)