Complexity of Derivative-Free Policy Optimization for Structured $\mathcal{H}_\infty$ Control
Authors: Xingang Guo, Darioush Keivan, Geir Dullerud, Peter Seiler, Bin Hu
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the numerical study to show the effectiveness of Algorithm 1. The left plot of Figure 1 displays the relative error trajectories of Algorithm 1 with an exact oracle for system dimensions nx = {10, 50, 100}. |
| Researcher Affiliation | Academia | Xingang Guo ECE, CSL UIUC xingang2@illinois.edu Darioush Keivan MSE, CSL UIUC dk12@illinois.edu Geir Dullerud MSE, CSL UIUC dullerud@illinois.edu Peter Seiler EECS UMich pseiler@umich.edu Bin Hu ECE, CSL UIUC binhu7@illinois.edu |
| Pseudocode | Yes | Algorithm 1: Derivative-Free Methods for Policy Optimization Problem (4) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | In addition, we conducted a comparison of our derivative-free method with the model-based methods HIFOO and Hinfstruct, using several benchmark examples from COMPleib [41]. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. The research focuses on control synthesis rather than traditional machine learning dataset evaluation. |
| Hardware Specification | Yes | All the experiments are performed on a desktop computer with a 3.7 GHz Intel i5-9600K processor. |
| Software Dependencies | No | The paper mentions using MATLAB and specific packages (HIFOO, Hinfstruct) for comparison, but does not provide specific version numbers for the software dependencies used to run their experiments. |
| Experiment Setup | Yes | For Algorthm 1, we set η = 1 10 3, δ = 1 10 4, and ϵ = 1 10 3. |