Heuristically Accelerated Q–Learning: A New Approach to Speed Up Reinforcement Learning

  • Reinaldo A. C. Bianchi
  • Carlos H. C. Ribeiro
  • Anna H. R. Costa
Conference paper

DOI: 10.1007/978-3-540-28645-5_25

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3171)
Cite this paper as:
Bianchi R.A.C., Ribeiro C.H.C., Costa A.H.R. (2004) Heuristically Accelerated Q–Learning: A New Approach to Speed Up Reinforcement Learning. In: Bazzan A.L.C., Labidi S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science, vol 3171. Springer, Berlin, Heidelberg

Abstract

This work presents a new algorithm, called Heuristically Accelerated Q–Learning (HAQL), that allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q–learning. A heuristic function \(\mathcal{H}\) that influences the choice of the actions characterizes the HAQL algorithm. The heuristic function is strongly associated with the policy: it indicates that an action must be taken instead of another. This work also proposes an automatic method for the extraction of the heuristic function \(\mathcal{H}\) from the learning process, called Heuristic from Exploration. Finally, experimental results shows that even a very simple heuristic results in a significant enhancement of performance of the reinforcement learning algorithm.

Keywords

Reinforcement Learning Cognitive Robotics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Reinaldo A. C. Bianchi
    • 1
    • 2
  • Carlos H. C. Ribeiro
    • 3
  • Anna H. R. Costa
    • 1
  1. 1.Laboratório de Técnicas InteligentesEscola Politécnica da Universidade de São PauloSão PauloBrazil
  2. 2.Centro Universitário da FEISão Bernardo do CampoBrazil
  3. 3.Instituto Tecnológico de AeronáuticaSão José dos CamposBrazil

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