Daniel Brown, Scott Niekum, Marek Petrik
Read the paper: arXiv Link.
This repository contains code to reproduce the experiments.
If you find this repository is useful in your research, please cite our paper:
@InProceedings{brown2020broil,
title = {Bayesian Robust Optimization for Imitation Learning},
author = {Brown, Daniel S. and Niekum, Scott and Petrik, Marek},
booktitle={Advances in neural information processing systems (NeurIPS)},
year={2020}
}
conda env create --file environment.yml
conda activate broil
python -m ipykernel install --user --name=broil
Before running any of the commands below, make sure to first activate the conda environment
conda activate broil
python machine_replacement.py
python machine_replacement_action_probs.py
python machine_replacement_gen_return_dists.py
python machine_replacement_plot_return_dists.py
To reproduce the ambiguous demonstration experiment run the following ipython notebook:
jupyter notebook AmbiguousDemos.ipynb
Make sure to change the kernel of the notebook to use the broil conda env by selecting from the dropdown menu: Kernel -> Change kernel -> broil.
Computational Stress Tests (see Appendix for details):
To reproduce the experiments and plots for the computational efficiency stress tests run:
python machine_replacement_experiment_stress_test_numrewards.py
python machine_replacement_experiment_stress_test_numstates.py
python grid_world_stress_test_numstates.py
Note that these will take a while to run. When they are complete you can generate plots using the following Jupyter notebook:
jupyter notebook StressTestPlots.ipynb
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