An Analytical Study of Utility Functions in Multi-Objective Reinforcement Learning
Authors: Manel Rodríguez Soto, Juan A Rodríguez-Aguilar, Maite López-Sánchez
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 manel.rodriguez@iiia.csic.es Juan A. Rodriguez-Aguilar Artificial Intelligence Research Institute (IIIA-CSIC) Bellaterra, Spain jar@iiia.csic.es Maite Lopez-Sanchez Universitat de Barcelona (UB) Barcelona, Spain maite_lopez@ub.edu |
| 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. |