{"id":32441377,"url":"https://github.com/jnlandu/deep-learning-indaba-ideathon-2024","last_synced_at":"2026-07-11T13:33:53.911Z","repository":{"id":318934562,"uuid":"1066471203","full_name":"jnlandu/Deep-Learning-Indaba-Ideathon-2024","owner":"jnlandu","description":"This is the repo for our project for publishing a paper in EVRP and DL","archived":false,"fork":false,"pushed_at":"2025-10-15T17:08:09.000Z","size":80197,"stargazers_count":2,"open_issues_count":10,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-07-11T13:33:51.126Z","etag":null,"topics":["evrp","indaba","xai"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jnlandu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-29T14:27:03.000Z","updated_at":"2026-03-26T15:40:48.000Z","dependencies_parsed_at":null,"dependency_job_id":"4d6f4a82-4e97-4e2a-beb9-82087e5c8268","html_url":"https://github.com/jnlandu/Deep-Learning-Indaba-Ideathon-2024","commit_stats":null,"previous_names":["jnlandu/deep-learning-indaba-ideathon-2024"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jnlandu/Deep-Learning-Indaba-Ideathon-2024","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jnlandu%2FDeep-Learning-Indaba-Ideathon-2024","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jnlandu%2FDeep-Learning-Indaba-Ideathon-2024/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jnlandu%2FDeep-Learning-Indaba-Ideathon-2024/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jnlandu%2FDeep-Learning-Indaba-Ideathon-2024/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jnlandu","download_url":"https://codeload.github.com/jnlandu/Deep-Learning-Indaba-Ideathon-2024/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jnlandu%2FDeep-Learning-Indaba-Ideathon-2024/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35364269,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-11T02:00:05.354Z","response_time":104,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["evrp","indaba","xai"],"created_at":"2025-10-26T01:56:01.788Z","updated_at":"2026-07-11T13:33:53.881Z","avatar_url":"https://github.com/jnlandu.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"#  Deep Learning Indaba, Ideathhon 2024.\n\nThis 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\".\n\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/ea74a66e-0583-4c13-b5df-0300761972d1\" /\u003e\n\n\n\n## An intro to EVRP\n\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/7a279dbd-5ad5-4623-8282-f3ef8d16f08a\" /\u003e\n\n\n## Illustration of Reinforcement Learning\n\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/a09aa225-c7ea-4c0e-813b-0f44f13ea2b7\" /\u003e\n\n\n\n## Methodology overview\nThe project methodology involves several key steps:\n\n\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/f25d6047-215c-49de-9120-aee7eaaf5e5f\" /\u003e\n\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/1db725ba-9526-40c6-84a3-248a29b5c6b3\" /\u003e\n\n\n\n### Project idea :\n\nThe 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. \n\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/bd3f9744-4791-4dde-826e-3ac319af6492\" /\u003e\n\n\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/15e52a98-4ba2-4867-8055-b516357d0ea4\" /\u003e\n\n\n\n## Research Gap:\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/daf1cb6a-a59d-4efd-8948-1b83ea86263f\" /\u003e\n\n\n\n## Organization\n- Google drive folder to share resources\n- Trello board to track progress\n- Whatsapp group for communication\n- GitHub repository for version control and collaboration\n\n\n\n### Our Team Members:\n- [Dimeth Nouicer](https://www.linkedin.com/in/dimeth-nouicer/) (Team Lead and Coordinator)\n- [Elie Mulamba](https://www.linkedin.com/in/eliemulamba/)(Evaluation Lead)\n- [Jeremie Mabiala](https://www.linkedin.com/in/jnlandu00a/)(Technical Lead)\n- [Imen Habibi](https://www.linkedin.com/in/habibi-imen-8b78b2216/)(Model development)\n- [Mame Diara Diouf](https://www.linkedin.com/in/mame-diarra-diouf-38875218a/)(Model development)\n- [Souleymane Diallo](https://www.linkedin.com/in/sdley/)(Model development)\n\n\u003cimg width=\"960\" height=\"540\" alt=\"Image\" src=\"https://github.com/user-attachments/assets/8d3c61ef-b000-4904-9194-7f0381a451c4\" /\u003e\n\n\n## References\n\n[1] Metz, C. (2017). *In two moves, AlphaGo and Lee Sedol redefined the future*. Accessed: 2024-10-15.\n\n[2] Metz, C. (2017). *How Google's AI viewed the move no human could understand*. Accessed: 2024-10-15.\n\n[3] Cruz, F., Young, C., Dazeley, R., \u0026 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.\n\n[4] Dazeley, R., Vamplew, P., \u0026 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\n\n[5] Nouicer, D., Msadaa, I. C., \u0026 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\n\n[6] Lin, B., Ghaddar, B., \u0026 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\n\n[7] Kool, W., van Hoof, H., \u0026 Welling, M. (2019). Attention, learn to solve routing problems! In *International Conference on Learning Representations*.\n\n[8] Milani, S., Topin, N., Veloso, M., \u0026 Fang, F. (2024). Explainable reinforcement learning: A survey and comparative review. *ACM Computing Surveys*, 56(7), Article 168. https://doi.org/10.1145/3616864\n\n[9] Wang, M., Wei, Y., Huang, X., \u0026 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\n\n[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\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjnlandu%2Fdeep-learning-indaba-ideathon-2024","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjnlandu%2Fdeep-learning-indaba-ideathon-2024","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjnlandu%2Fdeep-learning-indaba-ideathon-2024/lists"}