/Optimization-based-on-GNI-Index-For-multi-objective-bandits

This project is mainly used to reproduce the result in the paper of "Multi-objective Bandits: Optimizing the Generalized Gini Index"

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Optimization-based-on-GNI-Index-For-multi-objective-bandits

This project is mainly used to reproduce the result in the paper of "Multi-objective Bandits: Optimizing the Generalized Gini Index"

The code should be run in anaconda 3 and the package of cvxopt is needed.

In order to draw figures similar to Fig 2 in the paper, some parameters in the file of LearningML.py should be revised, the list of these parameters is listed as follows:

  1. K the number of arms
  2. D the number of dimentions
  3. learn_Rate Learning_rate
  4. Iteration_num number of iterations in MO-OGDE and MO-LP

Reference: Busa-Fekete, Robert, et al. "Multi-objective Bandits: Optimizing the Generalized Gini Index." arXiv preprint arXiv:1706.04933 (2017).