https://github.com/jnlandu/deep-learning-indaba-ideathon-2024
This is the repo for our project for publishing a paper in EVRP and DL
https://github.com/jnlandu/deep-learning-indaba-ideathon-2024
evrp indaba xai
Last synced: 8 months ago
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This is the repo for our project for publishing a paper in EVRP and DL
- Host: GitHub
- URL: https://github.com/jnlandu/deep-learning-indaba-ideathon-2024
- Owner: jnlandu
- Created: 2025-09-29T14:27:03.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-10-15T17:08:09.000Z (8 months ago)
- Last Synced: 2025-10-16T15:27:42.624Z (8 months ago)
- Topics: evrp, indaba, xai
- Language: Jupyter Notebook
- Homepage:
- Size: 76.5 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
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README
# Deep Learning Indaba, Ideathhon 2024.
This repo contains all the codes and resources for our winning project at the Deep Learning Ideathhon 2024. The goal of this project is to publish a research paper on the topic of "Transparent Decision-Making for Electric Vehicle Routing: Integrating DRL, GNN, and xAI".
## An intro to EVRP
## Illustration of Reinforcement Learning
## Methodology overview
The project methodology involves several key steps:
### Project idea :
The project idea is inspired by Dimeth Noucier who did her PhD in the field of electric vehicle routing using deep reinforcement learning. The idea is to enhance the transparency of the decision-making process of the DRL model by integrating explainable AI techniques.
## Research Gap:
## Organization
- Google drive folder to share resources
- Trello board to track progress
- Whatsapp group for communication
- GitHub repository for version control and collaboration
### Our Team Members:
- [Dimeth Nouicer](https://www.linkedin.com/in/dimeth-nouicer/) (Team Lead and Coordinator)
- [Elie Mulamba](https://www.linkedin.com/in/eliemulamba/)(Evaluation Lead)
- [Jeremie Mabiala](https://www.linkedin.com/in/jnlandu00a/)(Technical Lead)
- [Imen Habibi](https://www.linkedin.com/in/habibi-imen-8b78b2216/)(Model development)
- [Mame Diara Diouf](https://www.linkedin.com/in/mame-diarra-diouf-38875218a/)(Model development)
- [Souleymane Diallo](https://www.linkedin.com/in/sdley/)(Model development)
## References
[1] Metz, C. (2017). *In two moves, AlphaGo and Lee Sedol redefined the future*. Accessed: 2024-10-15.
[2] Metz, C. (2017). *How Google's AI viewed the move no human could understand*. Accessed: 2024-10-15.
[3] Cruz, F., Young, C., Dazeley, R., & Vamplew, P. (2022). Evaluating human-like explanations for robot actions in reinforcement learning scenarios. In *2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)* (pp. 894–901). IEEE.
[4] Dazeley, R., Vamplew, P., & Cruz, F. (2023). Explainable reinforcement learning for broad-XAI: a conceptual framework and survey. *Neural Computing and Applications*, 35, 16893–16916. https://doi.org/10.1007/s00521-023-08423-1
[5] Nouicer, D., Msadaa, I. C., & Grayaa, K. (2023). A novel routing solution for EV fleets: A real-world case study leveraging double DQNs and graph-structured data to solve the EVRPTW problem. *IEEE Access*, PP(99), 1-1. https://doi.org/10.1109/ACCESS.2023.3327324
[6] Lin, B., Ghaddar, B., & Nathwani, J. (2022). Deep reinforcement learning for the electric vehicle routing problem with time windows. *IEEE Transactions on Intelligent Transportation Systems*, 23(8), 11528-11538. https://doi.org/10.1109/TITS.2021.3105232
[7] Kool, W., van Hoof, H., & Welling, M. (2019). Attention, learn to solve routing problems! In *International Conference on Learning Representations*.
[8] Milani, S., Topin, N., Veloso, M., & Fang, F. (2024). Explainable reinforcement learning: A survey and comparative review. *ACM Computing Surveys*, 56(7), Article 168. https://doi.org/10.1145/3616864
[9] Wang, M., Wei, Y., Huang, X., & Gao, S. (2024). An end-to-end deep reinforcement learning framework for electric vehicle routing problem. *IEEE Internet of Things Journal*. https://doi.org/10.1109/JIOT.2024.3432911
[10] Glanois, C., Weng, P., Zimmer, M., et al. (2024). A survey on interpretable reinforcement learning. *Machine Learning*, 113, 5847–5890. https://doi.org/10.1007/s10994-024-06543-w