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..
Improved Strong Worst-case Upper Bounds for MDP Planning
Authors: Anchit Gupta, Shivaram Kalyanakrishnan
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We contribute to the theoretical analysis of MDP planning, which is the problem of computing an optimal policy for a given MDP. Specifically, we furnish improved strong worstcase upper bounds on the running time of MDP planning. |
| Researcher Affiliation | Academia | Anchit Gupta and Shivaram Kalyanakrishnan Department of Computer Science and Engineering, Indian Institute of Technology Bombay EMAIL |
| Pseudocode | No | The paper describes algorithms verbally and mathematically but does not include any 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 is theoretical and does not involve empirical training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |