Multi-objective Stochastic Linear Bandits

Code for AAAI2024 Paper: Hierarchize Pareto Dominance in Multi-objective Stochastic Linear Bandits

The repository contains:

oracle.py, simulators for multi-objective stochastic linear bandits. To apply to real-world dataset, rewrite methods observe_context and expected_reward for your subclass of the base class mo_contextual_bandit.
moslb.py, bandit algorithms, including ParetoUCB, MOSLB-PC, and MOSLB-PL; one can follow the implementation in "example.ipynb" for quick start.
utils.py, basic functions for the optimality, dominance under different preference, etc.

Reference

If you find our work helpful, please consider citing our paper:

@inproceedings{chengHierarchize,
  title={Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits},
  author={Cheng, Ji and Xue, Bo and Yi, Jiaxiang and Zhang, Qingfu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  pages={11489-11497},
  year={2024}
}

jicheng9617/moslb

Multi-objective Stochastic Linear Bandits

Reference