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.

It is great to see the kind of interest in applying machine learning, and specifically reinforcement learning, into real-world problems such as computer systems as presented in this paper. While the paper has no significant contributions on either a theoretical or algorithmic front, it does an important job at highlighting some of the issues in applying modern RL algorithms to real problems, and provides a necessary benchmarking environment for computer systems research specifically. The problem domains included have a wide variety of characteristics, from high-frequent real-time systems to very-long horizon problems, uniquely structured state and action spaces and both simulated and real environments (some other related work that could be added is [1]). Especially the latter is valuable to ground any research. Moreover, the authors provide an RL baseline result for each of the proposed tasks, and highlight some of the problematic characteristics of these tasks for RL specifically. There could be a more elaborate discussion of the results however.

The reviewers have each reviewed this paper carefully, and have taken the author response into account. There is clear consensus among them that this paper is a valuable contribution to the research community, both in helping to bring the application area of ML for systems environment more into the conversation and for providing a solid suite of benchmarks to foster further innovation within the community. I especially appreciate this aspect of helping to make the future research community more effective. In the author response, the authors describe several ways in which their paper will be revised to take reviewer feedback into account, and I expect this will be done for any final version of the paper.

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.

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. Papers published at the Neural Information Processing Systems Conference.