Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 17,431 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,819 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |