MetaLearnBO

This is the repository for Regret bounds for the experiments in meta Bayesian optimization with an unknown Gaussian process prior. The purpose is to show the reproducibility of the experimental results in the paper.

We have three domains:

1. Choosing an arm and a grasp for picking an object, with a fixed robot base pose
2. Choosing a robot base pose and grasp for picking an object
3. Synthetic continuous domain

To reproduce any of the results, in the MetaLearnBO folder, run

python run_experiments -domain [ag,gbp,synth] -bo [gpucb,pi] -algorithm [zbk,commonrs,rand,plain] 

where -domain option specifies the domain: ag refers to the arm-and-grasp domain, gpb refers to the grasp-base-pose domain, and synth refers to the continuous synthetic domain. -bo option specifies which Bayesian optimization acqusition to use: gpucb refers to Gaussian Process Upper Confidence Bounds, and pi refers to probabilistic improvement. -algorithm option specifies which prior estimation algorithm to use: zbk refers to our algorithm, PEM-BO, commonrs refers to the common response surface method, which we refer to a s TLSM-BO in our paper, rand refers to uniform random strategy, and plain refers to no prior estimation.

Citation

If you use our code, please consider citing our paper.

@inproceedings{wangkimNIPS2018,
    author={Zi Wang and Beomjoon Kim and Leslie Pack Kaelbling},
    title={Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior},
    booktitle={Neural Information Processing Systems (NeurIPS)},
    year={2018},
    url={http://people.csail.mit.edu/beomjoon/publications/zi-kim-nips18.pdf}
}

References

• Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior (Zi Wang*, Beomjoon Kim*, and Leslie Pack Kaelbling), In Neural Information Processing Systems (NeurIPS), 2018.
• Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior (Zi Wang*, Beomjoon Kim*, and Leslie Pack Kaelbling), arXiv.