Selective Dyna-Style Planning Under Limited Model Capacity
Authors: Zaheer Abbas, Samuel Sokota, Erin Talvitie, Martha White
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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. |