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..
The Power of Resets in Online Reinforcement Learning
Authors: Zak Mhammedi, Dylan J Foster, Alexander Rakhlin
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper has only mathematical congtent. There are no experiments in this paper. |
| Researcher Affiliation | Collaboration | Zakaria Mhammedi Google Research EMAIL Dylan J. Foster Microsoft Research EMAIL Alexander Rakhlin MIT EMAIL |
| Pseudocode | Yes | Algorithm 1 Sim Golf: Global Optimism via Local Simulator Access |
| Open Source Code | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Open Datasets | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Dataset Splits | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Hardware Specification | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Software Dependencies | No | This paper has only mathematical congtent. There are no experiments in this paper. |
| Experiment Setup | No | This paper has only mathematical congtent. There are no experiments in this paper. |