Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning
Authors: Manel RodrĂguez Soto, Juan A RodrĂguez-Aguilar, Maite LĂłpez-Sánchez
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 24,691 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,986 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As a fully theoretical paper, the paper does not include experiments. |
| Researcher Affiliation | Academia | Manel Rodriguez-Soto Artificial Intelligence Research Institute (IIIA-CSIC) Bellaterra, Spain EMAIL Juan A. Rodriguez-Aguilar Artificial Intelligence Research Institute (IIIA-CSIC) Bellaterra, Spain EMAIL Maite Lopez-Sanchez Universitat de Barcelona (UB) Barcelona, Spain EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks; its content is purely theoretical with definitions, theorems, and proofs. |
| Open Source Code | No | As a fully theoretical paper, the paper does not include experiments requiring code. The paper does not mention providing access to source code for the methodology described. |
| Open Datasets | No | As a fully theoretical paper, the paper does not conduct empirical studies or use datasets. |
| Dataset Splits | No | As a fully theoretical paper, the paper does not involve training, validation, or test dataset splits. |
| Hardware Specification | No | As a fully theoretical paper, it does not include any experiment, and therefore no hardware specifications are provided. |
| Software Dependencies | No | As a fully theoretical paper, it does not include experiments and thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | As a fully theoretical paper, the paper does not describe an experimental setup, hyperparameters, or training configurations. |