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..
Naive Exploration is Optimal for Online LQR
Authors: Max Simchowitz, Dylan Foster
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove new upper and lower bounds demonstrating that the optimal regret scales as eÎ( p d2udx T) |
| Researcher Affiliation | Academia | 1UC Berkeley 2Massachusetts Institute of Technology. Correspondence to: Max Simchowitz <EMAIL>. |
| Pseudocode | Yes | Our main algorithm, Algorithm 1, is detailed in Appendix H. It is an Îľ-greedy scheme that takes advantage of this principle. The full pseudocode and analysis are deferred to Appendix H, but we sketch the intuition here. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that its source code is open or publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and bounds for online LQR. It does not describe experiments run on a specific dataset or provide access information for a public dataset for training. |
| Dataset Splits | No | The paper is theoretical and focuses on mathematical proofs and bounds. It does not describe empirical experiments involving dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical proofs and bounds. It does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and bounds. It does not describe any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and bounds. It does not describe an empirical experimental setup with hyperparameters or training configurations. |