A policy gradient reinforcement learning algorithm with fuzzy function approximation

Dongbing Gu, Erfu Yang

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

3 Citations (Scopus)

Abstract

For complex systems, reinforcement learning has to be generalised from a discrete form to a continuous form due to large state or action spaces. In this paper, the generalisation of reinforcement learning to continuous state space is investigated by using a policy gradient approach. Fuzzy logic is used as a function approximation in the generalisation. To guarantee learning convergence, a policy approximator and a state action value approximator are employed for the reinforcement learning. Both of them are based on fuzzy logic. The convergence of the learning algorithm is justified.

Original languageEnglish
Title of host publicationIEEE International Conference on Robotics and Biomimetics, 2004. ROBIO 2004
Place of PublicationPiscataway, NJ.
PublisherIEEE
Pages936-940
Number of pages5
ISBN (Print)0780386148
DOIs
Publication statusPublished - 2004
Event2004 IEEE International Conference on Robotics and Biomimetics, ROBIO 2004 - Shenyang, China
Duration: 22 Aug 200426 Aug 2004

Conference

Conference2004 IEEE International Conference on Robotics and Biomimetics, ROBIO 2004
CountryChina
CityShenyang
Period22/08/0426/08/04

Keywords

  • fuzzy Q-learning
  • policy gradient method
  • reinforcement learning
  • learning convergence
  • approximation theory
  • convergence of numerical methods
  • functions
  • fuzzy sets
  • large scale systems
  • learning algorithms
  • state space methods

Fingerprint Dive into the research topics of 'A policy gradient reinforcement learning algorithm with fuzzy function approximation'. Together they form a unique fingerprint.

  • Cite this

    Gu, D., & Yang, E. (2004). A policy gradient reinforcement learning algorithm with fuzzy function approximation. In IEEE International Conference on Robotics and Biomimetics, 2004. ROBIO 2004 (pp. 936-940). IEEE. https://doi.org/10.1109/ROBIO.2004.1521910