CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 平行控制
Manifold Regularized Reinforcement Learning
Li, Hongliang1; Liu, Derong2; Wang, Ding3
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2018-04-01
Volume29Issue:4Pages:932-943
SubtypeArticle
AbstractThis paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method. The learned features are data-driven, and can be adapted to the geometry of the state space. Furthermore, the scheme provides a direct basis representation extension for novel samples during policy learning and control. The performance of the proposed scheme is evaluated on two benchmark control tasks, i.e., the inverted pendulum and the energy storage problem. Simulation results illustrate the concepts of the proposed scheme and show that it can obtain excellent performance.
KeywordAdaptive Dynamic Programming Approximate Dynamic Programming Approximate Policy Iteration (Api) Manifold Regularization Reinforcement Learning (Rl)
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2017.2650943
WOS KeywordTIME NONLINEAR-SYSTEMS ; VALUE FUNCTION APPROXIMATION ; SQUARES POLICY ITERATION ; DIMENSIONALITY REDUCTION ; LAPLACIAN FRAMEWORK ; GEOMETRIC FRAMEWORK ; ALGORITHMS ; REPRESENTATION ; MACHINES ; DESIGN
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000427859600014
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21992
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Tencent Inc, AI Platform Dept, Shenzhen 518057, Peoples R China
2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Hongliang,Liu, Derong,Wang, Ding. Manifold Regularized Reinforcement Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(4):932-943.
APA Li, Hongliang,Liu, Derong,&Wang, Ding.(2018).Manifold Regularized Reinforcement Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(4),932-943.
MLA Li, Hongliang,et al."Manifold Regularized Reinforcement Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.4(2018):932-943.
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