A New Distributed Reinforcement Learning Algorithm for Multiple Objective Optimization Problems

  • Carlos Mariano
  • Eduardo Morales
Conference paper

DOI: 10.1007/3-540-44399-1_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1952)
Cite this paper as:
Mariano C., Morales E. (2000) A New Distributed Reinforcement Learning Algorithm for Multiple Objective Optimization Problems. In: Monard M.C., Sichman J.S. (eds) Advances in Artificial Intelligence. Lecture Notes in Computer Science, vol 1952. Springer, Berlin, Heidelberg

Abstract

This paper describes a new algorithm, called MDQL, for the solution of multiple objective optimization problems. MDQL is based on a new distributed Q-learning algorithm, called DQL, which is also introduced in this paper. In DQL a family of independent agents, explo- ring different options, finds a common policy in a common environment. Information about action goodness is transmitted using traces over state- action pairs. MDQL extends this idea to multiple objectives, assigning a family of agents for each objective involved. A non-dominant criterion is used to construct Pareto fronts and by delaying adjustments on the rewards MDQL achieves better distributions of solutions. Furthermore, an extension for applying reinforcement learning to continuous functions is also given. Successful results of MDQL on several test-bed problems suggested in the literature are described.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Carlos Mariano
    • 1
  • Eduardo Morales
    • 2
  1. 1.Instituto Mexicano de Tecnología del AguaJiutepec, MorelosMEXICO
  2. 2.ITESM - Campus MorelosTemixco, MorelosMEXICO

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