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..
Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach
Authors: Swetha Ganesh, Vaneet Aggarwal
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The experimental results are not provided, since the focus of the paper is on theoretical sample complexity analysis. |
| Researcher Affiliation | Academia | Swetha Ganesh Purdue University, USA EMAIL Vaneet Aggarwal Purdue University, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Natural Actor-Critic with Batching |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments. |
| Dataset Splits | No | Our work is theoretical, and hence there are no experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments requiring code. |
| Experiment Setup | No | Our work is theoretical, and hence there are no experiments. |