A Hybrid Learning Strategy for Discovery of Policies of Action

  • Richardson Ribeiro
  • Fabrício Enembreck
  • Alessandro L. Koerich
Conference paper

DOI: 10.1007/11874850_31

Volume 4140 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Ribeiro R., Enembreck F., Koerich A.L. (2006) A Hybrid Learning Strategy for Discovery of Policies of Action. In: Sichman J.S., Coelho H., Rezende S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. Lecture Notes in Computer Science, vol 4140. Springer, Berlin, Heidelberg

Abstract

This paper presents a novel hybrid learning method and performance evaluation methodology for adaptive autonomous agents. Measuring the performance of a learning agent is not a trivial task and generally requires long simulations as well as knowledge about the domain. A generic evaluation methodology has been developed to precisely evaluate the performance of policy estimation techniques. This methodology has been integrated into a hybrid learning algorithm which aim is to decrease the learning time and the amount of errors of an adaptive agent. The hybrid learning method namely K-learning, integrates the Q-learning and K Nearest-Neighbors algorithm. Experiments show that the K-learning algorithm surpasses the Q-learning algorithm in terms of convergence speed to a good policy.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Richardson Ribeiro
    • 1
  • Fabrício Enembreck
    • 1
  • Alessandro L. Koerich
    • 1
  1. 1.Programa de Pós-Graduação em Informática Aplicada (PPGIA)Pontifícia Universidade Católica do ParanáCuritiba, ParanáBrasil