Skip to content

automl/DAC

master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
dac
 
 
 
 
 
 
 
 
 
 

DAC

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,
}

Installation using Anaconda

  1. conda create -n dac python=3.7
  2. conda activate dac
  3. cat requirements.txt | xargs -L 1 -n 1 pip install
  4. python setup.py install
  5. (optional) conda install jupyter

Example calls

  • 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:
    1. 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
    2. 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*

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

License

This repository is licensed under the Apache License 2.0

Repository Structure

  • 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
  • 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
  • Appendix.pdf
    The online appendix of the paper.
  • example_plots.ipynb
    Jupyter-Notebook to plot the example results

About

Dynamic Algorithm Configuration

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published