Chapter

Advances in Artificial Intelligence - IBERAMIA-SBIA 2006

Volume 4140 of the series Lecture Notes in Computer Science pp 268-277

A Hybrid Learning Strategy for Discovery of Policies of Action

  • Richardson RibeiroAffiliated withCarnegie Mellon UniversityPrograma de Pós-Graduação em Informática Aplicada (PPGIA), Pontifícia Universidade Católica do Paraná
  • , Fabrício EnembreckAffiliated withCarnegie Mellon UniversityPrograma de Pós-Graduação em Informática Aplicada (PPGIA), Pontifícia Universidade Católica do Paraná
  • , Alessandro L. KoerichAffiliated withCarnegie Mellon UniversityPrograma de Pós-Graduação em Informática Aplicada (PPGIA), Pontifícia Universidade Católica do Paraná

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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.