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..
Towards Tight Bounds on the Sample Complexity of Average-reward MDPs
Authors: Yujia Jin, Aaron Sidford
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove new upper and lower bounds for sample complexity of finding an ϵ-optimal policy of an infinite-horizon average-reward Markov decision process (MDP) given access to a generative model. |
| Researcher Affiliation | Academia | Yujia Jin 1 Aaron Sidford 1 1Management Science and Engineering, Stanford University, CA, United States. Correspondence to: Yujia Jin <EMAIL>. |
| Pseudocode | No | The paper describes algorithms but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is purely theoretical and does not describe software or methodology for which open-source code would be provided. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, so there is no mention of publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets, so there is no mention of training/validation/test splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is purely theoretical and does not describe any experimental setup or software dependencies with versions. |
| Experiment Setup | No | The paper is purely theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |