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{cheng2024hierarchize,
  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}
}