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-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
Authors: Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We theoretically explore the relationship between sample-efficiency and adaptivity in reinforcement learning. |
| Researcher Affiliation | Academia | Emmeran Johnson & Ciara Pike-Burke Department of Mathematics, Imperial College London, United Kingdom EMAIL Patrick Rebeschini Department of Statistics, University of Oxford, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-Batch Learning Model |
| Open Source Code | No | The paper does not mention or provide access to open-source code for its methodology. It is a theoretical paper. |
| Open Datasets | No | The paper is theoretical and does not use or refer to publicly available datasets for experiments. |
| Dataset Splits | No | The paper is theoretical and does not include experimental data splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware used. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup or hyperparameters. |