Toward a Classification of Finite Partial-Monitoring Games

  • Gábor Bartók
  • Dávid Pál
  • Csaba Szepesvári
Conference paper

DOI: 10.1007/978-3-642-16108-7_20

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6331)
Cite this paper as:
Bartók G., Pál D., Szepesvári C. (2010) Toward a Classification of Finite Partial-Monitoring Games. In: Hutter M., Stephan F., Vovk V., Zeugmann T. (eds) Algorithmic Learning Theory. ALT 2010. Lecture Notes in Computer Science, vol 6331. Springer, Berlin, Heidelberg

Abstract

In a finite partial-monitoring game against Nature, the Learner repeatedly chooses one of finitely many actions, the Nature responds with one of finitely many outcomes, the Learner suffers a loss and receives feedback signal, both of which are fixed functions of the action and the outcome. The goal of the Learner is to minimize its total cumulative loss. We make progress towards classification of these games based on their minimax expected regret. Namely, we classify almost all games with two outcomes: We show that their minimax expected regret is either zero, \(\widetilde{\Theta}(\sqrt{T})\), Θ(T2/3), or Θ(T) and we give a simple and efficiently computable classification of these four classes of games. Our hope is that the result can serve as a stepping stone toward classifying all finite partial-monitoring games.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Gábor Bartók
    • 1
  • Dávid Pál
    • 1
  • Csaba Szepesvári
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaCanada

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