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].
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. |