Cognitive Reinforcement Learning: An Interpretable Decision-Making for Virtual Driver

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE journal of radio frequency identification Pub Date : 2024-06-24 DOI:10.1109/JRFID.2024.3418649
Hao Qi;Enguang Hou;Peijun Ye
{"title":"Cognitive Reinforcement Learning: An Interpretable Decision-Making for Virtual Driver","authors":"Hao Qi;Enguang Hou;Peijun Ye","doi":"10.1109/JRFID.2024.3418649","DOIUrl":null,"url":null,"abstract":"The interpretability of decision-making in autonomous driving is crucial for the building of virtual driver, promoting the trust worth of artificial intelligence (AI) and the efficiency of human-machine interaction. However, current data-driven methods such as deep reinforcement learning (DRL) directly acquire driving policies from collected data, where the decision-making process is vague for safety validation. To address this issue, this paper proposes cognitive reinforcement learning that can both simulate the human driver’s deliberation and provide interpretability of the virtual driver’s behaviors. The new method involves cognitive modeling, reinforcement learning and reasoning path extraction. Experiments on the virtual driving environment indicate that our method can semantically interpret the virtual driver’s behaviors. The results show that the proposed cognitive reinforcement learning model combines the interpretability of cognitive models with the learning capability of reinforcement learning, providing a new approach for the construction of trustworthy virtual drivers.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal of radio frequency identification","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10570307/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

The interpretability of decision-making in autonomous driving is crucial for the building of virtual driver, promoting the trust worth of artificial intelligence (AI) and the efficiency of human-machine interaction. However, current data-driven methods such as deep reinforcement learning (DRL) directly acquire driving policies from collected data, where the decision-making process is vague for safety validation. To address this issue, this paper proposes cognitive reinforcement learning that can both simulate the human driver’s deliberation and provide interpretability of the virtual driver’s behaviors. The new method involves cognitive modeling, reinforcement learning and reasoning path extraction. Experiments on the virtual driving environment indicate that our method can semantically interpret the virtual driver’s behaviors. The results show that the proposed cognitive reinforcement learning model combines the interpretability of cognitive models with the learning capability of reinforcement learning, providing a new approach for the construction of trustworthy virtual drivers.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
认知强化学习:虚拟驾驶员的可解释决策
自动驾驶中决策的可解释性对于构建虚拟驾驶员、提升人工智能(AI)的信任价值和人机交互效率至关重要。然而,目前的数据驱动方法(如深度强化学习(DRL))直接从收集的数据中获取驾驶策略,决策过程在安全验证方面比较模糊。针对这一问题,本文提出了认知强化学习方法,既能模拟人类驾驶员的思考过程,又能提供虚拟驾驶员行为的可解释性。新方法包括认知建模、强化学习和推理路径提取。虚拟驾驶环境的实验表明,我们的方法可以从语义上解释虚拟驾驶员的行为。结果表明,所提出的认知强化学习模型结合了认知模型的可解释性和强化学习的学习能力,为构建可信赖的虚拟驾驶员提供了一种新方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
5.70
自引率
0.00%
发文量
0
期刊最新文献
Robust Low-Cost Drone Detection and Classification Using Convolutional Neural Networks in Low SNR Environments Overview of RFID Applications Utilizing Neural Networks A 920-MHz, 160-μW, 25-dB Gain Negative Resistance Reflection Amplifier for BPSK Modulation RFID Tag A Fully-Passive Frequency Diverse Lens-Enabled mmID for Precise Ranging and 2-Axis Orientation Detection in Next-Generation IoT and Cyberphysical Systems A Compact Slot-Based Bi-Directional UHF RFID Reader Antenna for Far-Field Applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1