Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

Jaeyeon Ahn

Last update: Mar 22, 2022

Related tags

Deep Learning GEBO

Overview

Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

This repo contains the implementation of GEBO algorithm.

Dependencies

Anaconda Python 3.7
torch==1.7.1
torch-geometric==1.7.2
networkx==2.6.3
GPy==1.10.0
scikit-learn==1.0.2
scipy==1.7.1
numpy==1.20.3
tqdm==4.32.1
pandas==1.3.4

To run the experiments,

  python main.py --func CalibEnv 
  python main.py --func RobotPush

where --func specifics the task problem. You can either use the bash file run.sh to run the experiments.

Acknowledgements

The code is built upon the source code of CoCaBO. We thank the authors for their provision of the code.

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Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

Related tags

Overview

Code Impementation for "Mold into a Graph: Efficient Bayesian Optimization over Mixed Spaces"

Dependencies

To run the experiments,

Acknowledgements

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