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..
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration
Authors: Priyank Agrawal, Jinglin Chen, Nan Jiang6566-6573
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper studies regret minimization with randomized value functions in reinforcement learning. In tabular ο¬nite-horizon Markov Decision Processes, we introduce a clipping variant of one classical Thompson Sampling (TS)-like algorithm, randomized least-squares value iteration (RLSVI). Our O(H2S AT) high-probability worst-case regret bound improves the previous sharpest worst-case regret bounds for RLSVI and matches the existing state-of-the-art worst-case TS-based regret bounds. |
| Researcher Affiliation | Academia | Priyank Agrawal*, Jinglin Chen*, Nan Jiang University of Illinois at Urbana-Champaign, Urbana, IL, 61801 EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 C-RLSVI |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for the methodology described. |
| Open Datasets | No | The paper is purely theoretical and does not involve the use of datasets for training. |
| Dataset Splits | No | The paper is purely theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not provide details about experimental setup, hyperparameters, or training settings. |