A Sufficient Statistic for Influence in Structured Multiagent Environments
Authors: Frans A. Oliehoek, Stefan Witwicki, Leslie P. Kaelbling
JAIR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As such, the contributions of this paper are of a theoretical nature: they provide a principled understanding of lossless abstractions in structured (multiagent) decision problems by providing a formal framework that gives a unified perspective on previous work, while at the same time providing new insights and extending the scope of applicability. The main technical result is the proof of sufficiency given in Section 6 |
| Researcher Affiliation | Collaboration | Frans A. Oliehoek is affiliated with Delft University of Technology (academic). Stefan Witwicki is affiliated with Nissan Technical Center North America (industry). Leslie P. Kaelbling is affiliated with Massachusetts Institute of Technology (academic). This mix indicates a collaboration. |
| Pseudocode | No | The paper focuses on theoretical contributions, definitions, theorems, and proofs. There are no explicitly labeled pseudocode or algorithm blocks, nor any structured step-by-step procedures formatted as code. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. It mentions applications in deep reinforcement learning and future work but does not offer its own code. |
| Open Datasets | No | The paper discusses conceptual examples like the 'House Search problem' and the 'planetary exploration domain' to illustrate its theoretical framework. However, it does not use or provide access information for any publicly available datasets for empirical evaluation within this paper. |
| Dataset Splits | No | The paper is theoretical and does not present empirical experiments that would involve dataset splits. It does not mention any specific datasets or their partitioning. |
| Hardware Specification | No | This paper is theoretical in nature and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental implementations. Consequently, there are no specific software dependencies or version numbers mentioned. |
| Experiment Setup | No | This paper focuses on theoretical contributions, providing formal definitions and proofs. It does not describe any empirical experiments, and therefore, no experimental setup details, including hyperparameters or training configurations, are provided. |