Welcome to the papers repository! This repository contains scientific work where I have participated. The documents here reflect my contributions to research in various domains.
The following papers are currently available in this repository:
-
Demystifying Reinforcement Learning - SHAP and Captum
- Using a real-world dataset, we use eXplainable AI approaches to explore the reasoning behind a Deep Reinforcement Learning Agent.
- The agent was tasked to decide which product it had to produce given several constaints regarding demand, buffer size and storage constaints.
-
Learning Simulation-based Digital Twins for Discrete Material Flow Systems
- This SLR explores different techniques to learn Digital Twins in the manufacturing domain.
This repository serves as a collection of my academic contributions. It is intended for researchers, students, and professionals interested in these topics.
- Browse the repository to find relevant papers.
- Click on a paper to view or download it.
- Cite appropriately if referencing any work.
At the moment, this repository is read-only and does not accept external contributions. If you have questions or feedback, feel free to open an issue.
For any inquiries or discussions related to the papers, please reach out via GitHub issues or other relevant channels.
Happy reading! 📚