This repository contains the code for a semester research project done with the Sycamore lab at EPFL. The report for the project can be found here. We tackle the problem of the decentralized optimization of a global objective by combining mechanism design and preferential bayesian optimization.

Run the following

conda env create -f environment.yml -n pref
conda activate pref

pip install -e .

The configuration for running experiments is managed by hydra in configs.

The entrypoint for all experiments is app.py.

For example, use the following command to run the main algorithm with a equilibrium oracle and an horizon of 10:

python app.py learning_algorithm=oracle horizon=10

For predefined experiments referenced in the report, use the following command:

python app.py -cd configs/experiments --config-name experiment-{number}

To perform more advanced inspections, you might want to create or use a script in /scripts.

We use pytest as our testing framework.

Run the tests using the following command:

cd tests && python -m pytest