On Convergence Rate of Adaptive Multiscale Value Function Approximation for Reinforcement Learning

Tao Li, Quanyan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a generic framework for devising an adaptive approximation scheme for value function approximation in reinforcement learning, which introduces multiscale approximation. The two basic ingredients are multiresolution analysis as well as tree approximation. Starting from simple refinable functions, multiresolution analysis enables us to construct a wavelet system from which the basis functions are selected adaptively, resulting in a tree structure. Furthermore, we present the convergence rate of our multiscale approximation which does not depend on the regularity of basis functions.

Original languageEnglish (US)
Title of host publication2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728108247
DOIs
StatePublished - Oct 2019
Event29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 - Pittsburgh, United States
Duration: Oct 13 2019Oct 16 2019

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2019-October
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
CountryUnited States
CityPittsburgh
Period10/13/1910/16/19

Keywords

  • Multiscale approximation
  • multiresolution analysis
  • n-term approximation
  • reinforcement learning
  • tree approximation
  • wavelets

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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