Sparsity in Partially Controllable Linear Systems
Authors: Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also corroborate these theoretical improvements over certaintyequivalent control through a simulation study. and We present a proof-of-concept empirical study, to demonstrate the end-to-end statistical advantages of leveraging sparsity in the LQR of a PC-LQ. and Figure 1 clearly shows experimental results. |
| Researcher Affiliation | Industry | 1Microsoft Research New York, NY. Correspondence to: Yonathan Efroni <jonathan.efroni@gmail.com>. |
| Pseudocode | Yes | Algorithm 1 Learning Optimal Policy of PC-LQ and Algorithm 2 Semiparametric Least Squares |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is available or provide a link to a code repository. |
| Open Datasets | No | Synthetic PC-LQ problems were generated with i.i.d. standard Gaussian entries (for all A1, A2, A3, A12, A32, B1); The paper uses synthetic data and does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes generating synthetic data and conducting 100 trials for evaluation, but does not specify train/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch with version 1.9, or specific solver versions). |
| Experiment Setup | Yes | Synthetic PC-LQ problems were generated with i.i.d. standard Gaussian entries (for all A1, A2, A3, A12, A32, B1); the diagonal blocks were normalized by their top singular values so that ρ(A1) = 1, and ρ(A2) = ρ(A3) = 0.9. and ...soft-thresholded semiparametric least-squares estimator from Algorithm 1 (with ϵ = 0.1)... |