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 [1].
On Gap-dependent Bounds for Offline Reinforcement Learning
Authors: Xinqi Wang, Qiwen Cui, Simon S. Du
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper presents a systematic study on gap-dependent sample complexity in ofο¬ine reinforcement learning. ... Lastly, we present nearly-matching lower bounds to complement our gap-dependent upper bounds. ... 1.1 Main Contributions We present novel analyses for the standard VI-LCB algorithm (Algorithm 2). ... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Researcher Affiliation | Academia | Xinqi Wang Institute for Interdisciplinary Information Sciences Tsinghua University EMAIL Qiwen Cui Paul G. Allen School of Computer Science Engineering University of Washington EMAIL Simon S. Du Paul G. Allen School of Computer Science Engineering University of Washington EMAIL |
| Pseudocode | Yes | Algorithm 1: VI-LCB ... Algorithm 2: Subsampled VI-LCB |
| Open Source Code | No | (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments, dataset usage, or training. The ethics checklist states: "(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]" |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or dataset splits for validation. No mention of validation splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments. The ethics checklist states: "(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]" |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments. Therefore, it does not list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training settings for empirical runs. |