Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
Authors: Asaf Cassel, Alon Cohen, Tomer Koren
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present new efficient algorithms that achieve, perhaps surprisingly, regret that scales only (poly)logarithmically with the number of steps in two scenarios: when only the state transition matrix A is unknown, and when only the stateaction transition matrix B is unknown and the optimal policy satisfies a certain non-degeneracy condition. On the other hand, we give a lower bound that shows that when the latter condition is violated, square root regret is unavoidable. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, Tel Aviv University 2Google Research, Tel Aviv. |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | No | The paper does not provide a statement about releasing source code or include a link to a code repository. |
| Open Datasets | No | This is a theoretical paper. No datasets, public or otherwise, are mentioned or used for empirical evaluation. |
| Dataset Splits | No | This is a theoretical paper. No dataset split information (training, validation, or testing) is provided as no empirical experiments are conducted. |
| Hardware Specification | No | This is a theoretical paper. No specific hardware specifications are mentioned as no empirical experiments are reported. |
| Software Dependencies | No | This is a theoretical paper. No specific software dependencies with version numbers are mentioned as no empirical experiments are reported. |
| Experiment Setup | No | This is a theoretical paper. No specific experimental setup details, hyperparameters, or training configurations are provided as no empirical experiments are conducted. |