Park: An open platform for learning-augmented computer systems
Name
NeurIPS-2019-park-an-open-platform-for-learning-augmented-computer-systems-Paper.pdf
Description
Published version
Size
1.05 MB
Format
Unknown
Checksum (MD5)
647c84516fa565b2b80e760408587319
Author(s)
Mao, Hongzi
Negi, Parimarjan
Narayan, Akshay
Wang, Hanrui
Yang, Jiacheng
Wang, Haonan
Marcus, Ryan
Addanki, Ravichandra
Khani, Mehrdad
He, Songtao
Date Issued
2019
Journal
Advances in Neural Information Processing Systems
Version
Final published version
Abstract
© 2019 Neural information processing systems foundation. All rights reserved. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://papers.nips.cc/paper/2019/hash/f69e505b08403ad2298b9f262659929a-Abstract.html
