Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares...

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Detalles Bibliográficos
Autores principales: Liu, Bo, Chen, Sanfeng, Li, Shuai, Liang, Yongsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3376585/
https://www.ncbi.nlm.nih.gov/pubmed/22736969
http://dx.doi.org/10.3390/s120302632

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3376585/
https://www.ncbi.nlm.nih.gov/pubmed/22736969
http://dx.doi.org/10.3390/s120302632

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