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)


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.
Number of pages5
ISBN (Print)0780386148
Publication statusPublished - 2004
Event2004 IEEE International Conference on Robotics and Biomimetics, ROBIO 2004 - Shenyang, China
Duration: 22 Aug 200426 Aug 2004


Conference2004 IEEE International Conference on Robotics and Biomimetics, ROBIO 2004


  • 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

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