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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
Authors: Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 37,095 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,046 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 | In this section, we prove an instance-dependent regret bound of Algorithm 1. Our algorithmic design comprises three innovative components: (1) a variance-based weighted regression scheme that can be applied to a wide range of function classes, (2) a subroutine for variance estimation, and (3) a planning phase that utilizes a pessimistic value iteration approach. Our algorithm enjoys a regret bound that has a tight dependency on the function class complexity and achieves minimax optimal instance-dependent regret when specialized to linear function approximation. |
| Researcher Affiliation | Academia | Qiwei Di1, Heyang Zhao1, Jiafan he1, Quanquan Gu1 1Department of Computer Science, University of California, Los Angeles EMAIL |
| Pseudocode | Yes | Algorithm 1 Pessimistic Nonlinear Least-Squares Value Iteration (PNLSVI) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper describes using a 'batch-dataset D' for offline RL, but does not name any specific public datasets or provide access information for any dataset used. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments with dataset splits, so no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper focuses on theoretical algorithm design and analysis, and does not provide details about an experimental setup, such as hyperparameters or training settings. |