Article thumbnail

Statistical Linearization for Value Function Approximation in Reinforcement Learning

By Matthieu Geist

Abstract

Reinforcement learning (RL) is a machine learning answer to the optimal control problem. It consists in learning an optimal control policy through interactions with the system to be controlled, the quality of this policy being quantified by the so-called value function. An important RL subtopic is to approximate this function when the system is too large for an exact representation. This paper presents statistical-linearization-based approaches to estimate such functions. Compared to more classical approaches, this allows considering nonlinear parameterizations as well as the Bellman optimality operator, which induces some differentiability problems. Moreover, the statistical point of view adopted here allows considering colored observation noise models instead of the classical white one; in RL, this can provide useful.

Year: 2012
OAI identifier: oai:CiteSeerX.psu:10.1.1.207.6572
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.metz.supelec.fr/met... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.