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..
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
Authors: Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on several stochastic control tasks confirm the efficacy of our low-rank algorithms. |
| Researcher Affiliation | Academia | Devavrat Shah EECS, MIT EMAIL Dogyoon Song EECS, MIT EMAIL Zhi Xu EECS, MIT EMAIL EECS, MIT EMAIL |
| Pseudocode | Yes | We provide a narrative overview of the algorithm; the pseudo-code can be found in Appendix A. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using 'several stochastic control tasks' and that they 'first discretize the spaces into very fine grid and run standard value iteration to obtain a proxy of Q'. However, it does not provide concrete access information (links, DOIs, formal citations) to publicly available datasets used for training. |
| Dataset Splits | No | The paper does not explicitly provide specific details about training, validation, or test dataset splits (e.g., percentages, sample counts, or citations to predefined splits) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | The detailed setup can be found in Appendix H. In short, we first discretize the spaces into very fine grid and run standard value iteration to obtain a proxy of Q. The proxy has a very small approximate rank in all tasks; we hence use r = 10 for our experiments. As mentioned, we simply select r states and r actions that are far from each other in their respective metric. |