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Incremental Learning Framework for Autonomous Robots based on Q-learning and the Adaptive Kernel Linear Model
Hu YM(胡艳明)1,2,3; Li DC(李德才)1,2; He YQ(何玉庆)1,2; Han JD(韩建达)1,2,4
Department机器人学研究室
Source PublicationIEEE Transactions on Cognitive and Developmental Systems
ISSN2379-8920
2019
Indexed ByEI
EI Accession number20200107979383
Contribution Rank1
Funding OrganizationNature Sciences Foundation of China (Grant Nos.U1608253, 61473282) ; Chinese Academy of Sciences (Grant No. 6141A01061601)
Keywordincremental learning path planning Q-learning autonomous robots recursive least squares algorithm L2-norm
Abstract

The performance of autonomous robots in varying environments needs to be improved. For such incremental improvement, here we propose an incremental learning framework based on Q-learning and the adaptive kernel linear (AKL) model. The AKL model is used for storing behavioral policies that are learned by Q-learning. Both the structure and parameters of the AKL model can be trained using a novel L2-norm kernel recursive least squares (L2-KRLS) algorithm. AKL model initially without nodes and gradually accumulates content. The proposed framework allows to learn new behaviors without forgetting the previous ones. A novel local -greedy policy is proposed to speed the convergence rate of Q-learning. It calculates the exploration probability of each state for generating and selecting more important training samples. The performance of our incremental learning framework was validated in two experiments. A curve fitting example shows that the L2-KRLS based AKL model is suitable for incremental learning. The second experiment is based on robot learning tasks. The results show that our framework can incrementally learn behaviors in varying environments. Local -greedy policy-based Q-learning is faster than existing Q-learning algorithms.

Language英语
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26185
Collection机器人学研究室
Corresponding AuthorHe YQ(何玉庆)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016, Shenyang, Liaoning Province, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, 110016, Shenyang, Liaoning Province, China
3.University of Chinese Academy of Sciences, 100049, Beijing, China
4.College of Artificial Intelligence, Nankai University, 300071, Tianjing,China.
Recommended Citation
GB/T 7714
Hu YM,Li DC,He YQ,et al. Incremental Learning Framework for Autonomous Robots based on Q-learning and the Adaptive Kernel Linear Model[J]. IEEE Transactions on Cognitive and Developmental Systems,2019.
APA Hu YM,Li DC,He YQ,&Han JD.(2019).Incremental Learning Framework for Autonomous Robots based on Q-learning and the Adaptive Kernel Linear Model.IEEE Transactions on Cognitive and Developmental Systems.
MLA Hu YM,et al."Incremental Learning Framework for Autonomous Robots based on Q-learning and the Adaptive Kernel Linear Model".IEEE Transactions on Cognitive and Developmental Systems (2019).
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