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..
Selective Dyna-Style Planning Under Limited Model Capacity
Authors: Zaheer Abbas, Samuel Sokota, Erin Talvitie, Martha White
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We address the second problem by empirically investigating selective planning in the context of model-based value expansion (MVE), a planning algorithm that uses a learned model to construct multi-step TD targets (Feinberg et al., 2018). |
| Researcher Affiliation | Academia | 1The University of Alberta and the Alberta Machine Intelligence Institute (Amii) 2Harvey Mudd College. |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for source code related to the methodology described. |
| Open Datasets | No | The paper describes generating a dataset for a regression problem and using the Acrobot environment, but it does not provide concrete access information (link, DOI, repository, or formal citation with author/year) for publicly available or open datasets specifically used in their experiments. |
| Dataset Splits | No | The paper mentions '5,000 training examples' for a synthetic dataset but does not provide specific details on training, validation, and test splits (e.g., percentages, sample counts, or citations to predefined splits) for its experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., 'PyTorch 1.9') needed to replicate the experiment. |
| Experiment Setup | No | The paper states that experimental details, including parameter configurations and hyperparameters, are detailed in Appendices A, C, and D, indicating they are not present in the main text. |