Scalable Kernel Inverse Optimization
Authors: Youyuan Long, Tolga Ok, Pedro Zattoni Scroccaro, Peyman Mohajerin Mohajerin Esfahani
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate the generalization capabilities of the proposed KIO model and the effectiveness of the SSO algorithm through learning-from-demonstration tasks on the Mu Jo Co benchmark. |
| Researcher Affiliation | Academia | Youyuan Long Delft Center for Systems and Control Delft University of Technology The Netherlands longyouyuan432@gmail.comTolga Ok Delft Center for Systems and Control Delft University of Technology The Netherlands T.Ok@tudelft.nlPedro Zattoni Scroccaro Delft Center for Systems and Control Delft University of Technology The Netherlands P.Zattoni Scroccaro@tudelft.nlPeyman Mohajerin Esfahani Delft Center for Systems and Control Delft University of Technology The Netherlands P.Mohajerin Esfahani@tudelft.nl |
| Pseudocode | Yes | Algorithm 1 Sequential Selection Optimization (SSO) |
| Open Source Code | Yes | To foster reproducibility and further research, we provide an opensource implementation of the proposed KIO model and the SSO algorithm, along with the source code of the experiments in Github1. 1https://github.com/Longyouyuan/Scalable-Kernel-Inverse-Optimization |
| Open Datasets | Yes | KIO is implemented in its simplified version (9), incorporating a Gaussian kernel, and tested on continuous control datasets from the D4RL benchmark [18]. [18] Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning, 2020. |
| Dataset Splits | No | The paper states that the model is 'trained using the SSO Algorithm 1' and 'assessed over 100 test episodes,' but it does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts) or cite a specific standard split methodology for D4RL. |
| Hardware Specification | No | The paper mentions that solving certain problems 'requires up to 256GB of memory,' indicating a memory requirement, but it does not specify the actual hardware used such as GPU/CPU models, types, or speeds. |
| Software Dependencies | Yes | The paper mentions using 'CVXPY [13]' and 'off-the-shelf solvers, such as MOSEK [3].' MOSEK is cited with a version number: 'Mosek Ap S. Mosek optimization toolbox for matlab. User s Guide and Reference Manual, Version, 4:1, 2019.' |
| Experiment Setup | Yes | All hyperparameters used in this experiment for KIO are listed in Appendix B. |