Data-driven vs model-driven imitative learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the fundamental problems of an interconnected interactive system is the huge amounts of data that are being generated by every entity. Unfortunately, what we seek is not data but information, and therefore, a growing bottleneck is exactly how to extract and learn useful information from data. In this paper, the information-Theoretic learning in data-driven games is studied. This learning shows that the imitative Boltzmann-Gibbs strategy is the maximizer of the perturbed payoff where the perturbation function is the relative entropy from the previous strategy to the current one. In particular, the imitative strategy is the best learning scheme with the respect to data-driven games with cost of moves. Based on it, the classical imitative Boltzmann-Gibbs learning in data-driven games is revisited. Due to communication complexity and noisy data measurements, the classical imitative Boltzmann-Gibbs cannot be applied directly in situations were only numerical values of player's own payoff is measured. A combined fully distributed payoff and strategy imitative learning (CODIPAS) is proposed. Connections between the rest points of the resulting game dynamics, equilibria are established.

Original languageEnglish (US)
Title of host publicationProceedings of 2017 IEEE 6th Data Driven Control and Learning Systems Conference, DDCLS 2017
EditorsMingxuan Sun, Huijun Gao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages22-29
Number of pages8
ISBN (Electronic)9781509054619
DOIs
StatePublished - Oct 13 2017
Event6th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2017 - Chongqing, China
Duration: May 26 2017May 27 2017

Publication series

NameProceedings of 2017 IEEE 6th Data Driven Control and Learning Systems Conference, DDCLS 2017

Other

Other6th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2017
CountryChina
CityChongqing
Period5/26/175/27/17

Keywords

  • Data-driven Learning
  • Distributed Systems
  • Game Dynamics
  • Imitation
  • Noisy Games

ASJC Scopus subject areas

  • Computational Mechanics
  • Artificial Intelligence
  • Computer Networks and Communications

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  • Cite this

    Tembine, H. (2017). Data-driven vs model-driven imitative learning. In M. Sun, & H. Gao (Eds.), Proceedings of 2017 IEEE 6th Data Driven Control and Learning Systems Conference, DDCLS 2017 (pp. 22-29). [8067719] (Proceedings of 2017 IEEE 6th Data Driven Control and Learning Systems Conference, DDCLS 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS.2017.8067719