Anzeige
Erschienen in:
2016 | OriginalPaper | Buchkapitel
A Kernel-Based Sarsa(\(\lambda \)) Algorithm with Clustering-Based Sample Sparsification
verfasst von : Haijun Zhu, Fei Zhu, Yuchen Fu, Quan Liu, Jianwei Zhai, Cijia Sun, Peng Zhang
Erschienen in: Neural Information Processing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
In the past several decades, as a significant class of solutions to the large scale or continuous space control problems, kernel-based reinforcement learning (KBRL) methods have been a research hotspot. While the existing sample sparsification methods of KBRL exist the problems of low time efficiency and poor effect. For this problem, we propose a new sample sparsification method, clustering-based novelty criterion (CNC), which combines a clustering algorithm with a distance-based novelty criterion. Besides, we propose a clustering-based selective kernel Sarsa(\(\lambda \)) (CSKS(\(\lambda \))) on the basis of CNC, which applies Sarsa(\(\lambda \)) to learning parameters of the selective kernel-based value function based on local validity. Finally, we illustrate that our CSKS(\(\lambda \)) surpasses other state-of-the-art algorithms by Acrobot experiment.