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].
Structural Results for Cooperative Decentralized Control Models
Authors: Jilles Steeve Dibangoye, Olivier Buffet, Olivier Simonin
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper introduces a general methodology structural analysis for the design of optimality-preserving concise policies and value functions, which will eventually lead to the development of ef๏ฌcient theory and algorithms. |
| Researcher Affiliation | Academia | Jilles S. Dibangoye Inria CITI Lyon, France EMAIL Olivier Buffet Inria Nancy, France EMAIL Olivier Simonin Inria CITI Lyon, France EMAIL |
| Pseudocode | Yes | Algorithm 1: The OHSVI algorithm. and Algorithm 2: The Structural Analysis. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a publicly available dataset with concrete access information. It refers to |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with datasets, thus no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and discusses algorithms and analysis. It does not provide details on specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on methodology and structural analysis. It does not describe an experimental setup with specific hyperparameters or training configurations. |