Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
Authors: Emmeran Johnson, Ciara Pike-Burke, Patrick Rebeschini
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 {emmeran.johnson17,c.pike-burke}@imperial.ac.uk Patrick Rebeschini Department of Statistics, University of Oxford, United Kingdom patrick.rebeschini@stats.ox.ac.uk |
| 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. |