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Improving Gaussian Process Value Function Approximation in Policy Gradient Algorithms

  • Hunor Jakab
  • Lehel Csató
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6792)

Abstract

The use of value-function approximation in reinforcement learning (RL) problems is widely studied, the most common application of it being the extension of value-based RL methods to continuous domains. Gradient-based policy search algorithms can also benefit from the availability of an estimated value-function, as this estimation can be used for gradient variance reduction. In this article we present a new value function approximation method that uses a modified version of the Kullback–Leibler (KL) distance based sparse on-line Gaussian process regression. We combine it with Williams’ episodic REINFORCE algorithm to reduce the variance of the gradient estimates. A significant computational overload of the algorithm is caused by the need to completely re-estimate the value-function after each gradient update step. To overcome this problem we propose a measure composed of a KL distance–based score and a time dependent factor to exchange obsolete basis vectors with newly acquired measurements. This method leads to a more stable estimation of the action value-function and also reduces gradient variance. Performance and convergence comparisons are provided for the described algorithm, testing it on a dynamic system control problem with continuous state-action space.

Keywords

Reinforcement learning policy gradient methods Gaussian processes value function estimation control problems 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hunor Jakab
    • 1
    • 2
  • Lehel Csató
    • 1
    • 2
  1. 1.Babeş-Bolyai UniversityCluj-NapocaRomania
  2. 2.Eötvös Loránd UniversityBudapestHungary

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