Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
Authors: Junyan Liu, Yunfan Li, Ruosong Wang, Lin Yang
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is theoretically oriented and does not conduct any experiment. |
| Researcher Affiliation | Academia | Junyan Liu University of Washington EMAIL Yunfan Li University of California, Los Angeles EMAIL Ruosong Wang CFCS and School of Computer Science Peking University EMAIL Lin F. Yang University of California, Los Angeles EMAIL |
| Pseudocode | Yes | Algorithm 1 Elimination framework for ULI Algorithm 2 PE with adaptive barycentric spanner Algorithm 3 Tabular Episodic MDPs with ULI guarantee Algorithm 4 Uniform estimation for value functions Algorithm 5 Construct estimated value function |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code release for the methodology described, nor does it provide a specific repository link. The NeurIPS checklist indicates "NA" for code access, stating "This paper is theoretically oriented and does not conduct any experiment.". |
| Open Datasets | No | The paper is theoretical and does not perform experiments with datasets, thus no training dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments, thus no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments or their software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include an experimental setup with specific hyperparameters or training configurations. |