News
Awards
Competition Awards
Abstract
We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems. Using RL for improving the performance of systems has a lot of potential, but is also in many ways very different from, for example, using RL for games. Thus, in this work we first discuss the unique challenges RL for systems has, and then propose Park an open extensible platform, which makes it easier for ML researchers to work on systems problems. Currently, Park consists of 12 real world system-centric optimization problems with one common easy to use interface. Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work.
Framework Overview
Video
Citation
@inproceedings{NEURIPS2019_f69e505b,
author = {Mao, Hongzi and Negi, Parimarjan and Narayan, Akshay and Wang, Hanrui and Yang, Jiacheng and Wang, Haonan and Marcus, Ryan and addanki, ravichandra and Khani Shirkoohi, Mehrdad and He, Songtao and Nathan, Vikram and Cangialosi, Frank and Venkatakrishnan, Shaileshh and Weng, Wei-Hung and Han, Song and Kraska, Tim and Alizadeh, Dr.Mohammad},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Park: An Open Platform for Learning-Augmented Computer Systems},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/f69e505b08403ad2298b9f262659929a-Paper.pdf},
volume = {32},
year = {2019}
}
Media
Acknowledgment
We thank the anonymous NeurIPS reviewers for their constructive feedback. This work was funded in part by the NSF grants CNS-1751009, CNS-1617702, a Google Faculty Research Award, an AWS Machine Learning Research Award, a Cisco Research Center Award, an Alfred P. Sloan Research Fellowship, and sponsors of the MIT DSAIL lab. This work was supported by Analog Devices Inc., a member of the Medical Electronic Device Realization Center (MEDRC), National Science Foundation, Qualcomm Innovation Fellowship and MIT-IBM Watson AI Lab.
