Code/Supplementary for the ECAI2020 paper
Dynamic Algorithm Configuration:
Foundation of a New Meta-Algorithmic Framework
If you used the presented benchmarks or framework in one of your research projects, please cite us:
@inproceedings{biedenkapp-ecai20,
author = {A. Biedenkapp and H. F. Bozkurt and T. Eimer and F. Hutter and M. Lindauer},
title = {Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework},
booktitle = {Proceedings of the Twenty-fourth European Conference on Artificial Intelligence ({ECAI}'20)},
year = {2020},
month = jun,
}
- conda create -n dac python=3.7
- conda activate dac
- cat requirements.txt | xargs -L 1 -n 1 pip install
- python setup.py install
- (optional) conda install jupyter
- Train 0.1-greedy tabular Q-learning on 1D-sigmoids with trinary actions for 104 episodes:
python dac/train/train_other.py --seed 0 -r 1 -n 10000 --epsilon_decay const -e 0.1 -l 1. --env 1D3M --out-dir 1D-trinary-action-Sigmoid
- Train DDQN on 1D-sigmoids with trinary actions for 105/104 steps/episodes:
python dac/train/train_chainer_agent_on_toy.py --eval-n-runs 10 --eval-interval 10 --checkpoint_frequency 1000 --outdir 1D-trinary-action-Sigmoid/DQN --seed 0 --scenario 1D3M --steps 100000
- Run PS-SMAC on 1D-sigmoids with trinary actions for 104 episodes:
- Find well performing parameter sequence:
python dac/train/train_other.py --seed 0 -r 1 -n 10000 --epsilon_decay const -e 0.1 -l 1. --env 1D3M --out-dir 1D-trinary-action-Sigmoid --bo
- Validate the found sequence:
python dac/train/train_other.py --seed 0 -r 1 -n 10000 --epsilon_decay const -e 0.1 -l 1. --env 1D3M --out-dir 1D-trinary-action-Sigmoid --bo --validate-bo 1D-trinary-action-Sigmoid/smac3*/run*
- Find well performing parameter sequence:
We provide a jupyter notebook with which you can inspect your own runs or the example results provided in example-results-1D-trinary-action-Sigmoid
This repository is licensed under the Apache License 2.0
- cmds:
Contains many more commands to run sigmoid experiments from the paper - dac
- envs:
Contains all instance features and code for the presented white-box benchmarks - train:
Contains code to train all the agents presented in the paper
- envs:
- example-results-1D-trinary-action-Sigmoid
- DQN:
Result of the above example DQN call - smac3-output:
Results of the call of PS-SMAC - tabular:
Result of the above example tabular call
- DQN:
- Appendix.pdf
The online appendix of the paper. - example_plots.ipynb
Jupyter-Notebook to plot the example results