RankedCUCB: Ranked Prioritization of Groups in Combinatorial Bandit Allocation

Lily Xu, 2022

This code implements and evaluates algorithms for the paper "Ranked Prioritization of Groups in Combinatorial Bandit Allocation" from AAMAS-22. In this paper we introduce RankedCUCB, a multi-armed bandit approach to select combinatorial actions that optimize the prioritization-based objective introduced. This code also implements and compares against a variety of baselines.

@inproceedings{xu2022ranked,
  title={Ranked Prioritization of Groups in Combinatorial Bandit Allocation},
  author={Xu, Lily and Biswas, Arpita and Fang, Fei and Tambe, Milind},
  booktitle={Proc.~31st International Joint Conference on Artificial Intelligence (IJCAI-22)},
  year={2022},
}

This project is licensed under the terms of the MIT license.

Usage

To execute the code and run experiments comparing RankedCUCB and the baselines, run:

python driver.py

To vary the settings, use the options:

python driver.py -N 25 -B 5 -G 5 -T 500 -l 0.4 -s 0 -V

The options are

• N - number of arms (e.g., targets, ports, or neighborhoods in the wildlife, COVID testing, or mobile health domains)
• B - budget (total number of resources to spend)
• G - number of demographic groups (e.g., wildlife species or human age group)
• D - domain {synthetic, wildlife}
• T - horizon
• l - lambda
• s - random seed
• V - whether to use verbose output (default False)

Files

• driver.py - main script to execute one set of experiments
• adversary.py - set up reward functions (adversary.py from AAAI-21 paper on Dual-Mandate Patrols)
• ranked_cucb.py - abstract class for RankedCUCB objective + algorithm
• ranked_linear.py - instantiated class for the linear objective + algoroithm
• get_domain.py - sets up the two experimental domains (wildlife and synthetic). Note that we are
• process_results.py - after running full set of experiments with different seeds, this script will consolidate the CSVs from each domain, computing mean and standard error, to be used in the paper

Requirements

• python==3.7.4
• numpy==1.17.2
• pandas==0.25.1
• matplotlib==3.1.1
• scipy==1.3.1
• pytorch==1.4.0
• scikit-learn==0.21.3
• gurobi==9.1.2