Online Linear Quadratic Control
Authors: Alon Cohen, Avinatan Hasidim, Tomer Koren, Nevena Lazic, Yishay Mansour, Kunal Talwar
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7. Experiments We demonstrate our approach on the problem of regulating conditions inside a data center (DC) server floor in the presence of time-varying power costs. We learn system dynamics from a real data center, but vary the costs and run algorithms in simulation. ... The normalized regret 1 T RT of to the two strategies is shown in Fig. 2. |
| Researcher Affiliation | Collaboration | 1Google Research, Tel Aviv 2Technion Israel Inst. of Technology 3Bar Ilan University 4Google Brain, Mountain View 5Tel Aviv University. Correspondence to: Alon Cohen <aloncohen@google.com>, Tomer Koren <tkoren@google.com>. |
| Pseudocode | Yes | Algorithm 1 Online LQ Controller |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | We learn system dynamics from a real data center, but vary the costs and run algorithms in simulation. We learn a linear approximation (A, B) of the dynamics in the operating range of interest on 4h of exploratory data with controls following a random walk. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We learn system dynamics from a real data center, but vary the costs and run algorithms in simulation. ... We amplify the noise by a factor of 5. We set the diagonal coefficients of Qt corresponding to the most recent (normalized) sensor measurements to 1 and remaining coefficients to 0, and keep Qt = Q constant throughout the experiment. We set diagonal coefficients of Rt corresponding to water usage (valve command) to 1 throughout, and all coefficients corresponding to power usage (fan speed) to rt. We generate rt by (a) i.i.d sampling a uniform distribution on [0.1, 1], and (b) using a random walk restricted to [0.1, 1] taking steps of size 0.1, 0.1, 0 with probabilities 0.1, 0.1, 0.8 respectively. We run the FLL algorithm on this problem with the following modifications: we set Qp 1 = Q, and Rp 1 = Ik, an upper bound on Rt. |