No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities

This code repository accompanies the paper "No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities".

Requirements

1. Linux machine (experiments were run on Ubuntu 20.04.4 LTS)
2. Python >= 3.7
3. NumPy >= 1.21.5 (scipydirect requires NumPy to be installed first)
4. A Fortran compiler for scipydirect (if using Anaconda, this can be easily installed with conda install -c conda-forge gfortran)

Setup

In the main directory, run the following command to install the required libraries.

pip install -r requirements.txt

Experiments

To run individual experiments for specific seeds, the following commands may be used for pure and mixed NE respectively. GAME can be one of {rand, gan, bcad}. ACQ can be one of {ucb_pne, ucb_pne_noexplore, prob_eq, BN} for pure NE, and one of {ucb_mne, ucb_mne_noexplore, max_ent_mne, random_mne} for mixed NE. SEED can be any integer.

python pnebo.py with {GAME} acq_name={ACQ} seed={SEED}

python mnebo.py with {GAME} acq_name={ACQ} seed={SEED}

Alternatively, to run experiments in the background with seeds in the range START-END (inclusive), the following command may be used. TYPE can be one of {pne, mne}.

./run.sh {TYPE} {GAME} {ACQ} {START} {END}

To run the timing experiments for pure NE, run the following command:

python timing.py with {GAME}

Results

Once the experiments have completed, the following commands may be used to plot the regrets. The seeds used must be put in pne_results.py and mne_results.py, otherwise they will assume you used the seeds declared in the paper.

python pne_results.py with {GAME}

python mne_results.py with {GAME}