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 [1].
On Structural Properties of MDPs that Bound Loss Due to Shallow Planning
Authors: Nan Jiang, Satinder Singh, Ambuj Tewari
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results with randomly generated MDPs are used to validate qualitative properties of our theoretical bounds for shallow planning. |
| Researcher Affiliation | Academia | 1Computer Science and Engineering, University of Michigan 2Department of Statistics, University of Michigan |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper uses 'randomly generated MDPs' according to specified schemes (fixed, binom, ring) rather than a publicly available or open dataset with a specific access link or citation. |
| Dataset Splits | No | The paper describes generating and sampling MDPs for empirical validation of theoretical bounds, but it does not specify traditional training, validation, or test dataset splits for a machine learning model or algorithm. |
| Hardware Specification | No | The paper discusses computational effort but does not provide specific details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | Throughout the experiments we use ฮณeval = 0.995 and ฮณ = 0, 0.01, . . . , 0.99. |