Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations
Despite the substantial advancements in reinforcement learning (RL) in recent years, ensuring trustworthiness remains a formidable challenge when applying this technology to safety-critical autonomous driving domains. One pivotal bottleneck is that well-trained driving policy models may be particula...
Main Authors: | He, Xiangkun, Huang, Wenhui, Lv, Chen |
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Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2024
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/179385 |
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