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..
Abstraction Selection in Model-based Reinforcement Learning
Authors: Nan Jiang, Alex Kulesza, Satinder Singh
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Existing approaches have theoretical guarantees only under strong assumptions on the domain or asymptotically large amounts of data, but in this paper we propose a simple algorithm based on statistical hypothesis testing that comes with a finite-sample guarantee under assumptions on candidate abstractions. Our algorithm trades off the low approximation error of finer abstractions against the low estimation error of coarser abstractions, resulting in a loss bound that depends only on the quality of the best available abstraction and is polynomial in planning horizon. |
| Researcher Affiliation | Academia | Nan Jiang EMAIL Alex Kulesza EMAIL Satinder Singh EMAIL Computer Science & Engineering, University of Michigan |
| Pseudocode | Yes | Algorithm 1 Compare Pair(D, H, δ) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology described, nor does it provide links to a code repository. |
| Open Datasets | No | The paper is theoretical and refers to a 'dataset D' generically as part of its theoretical model, but it does not specify or use any particular named, publicly available dataset for experiments. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, therefore it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software names with version numbers that would be required to reproduce experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |