Hopefully, this is the complete list of all publications that have used Flatland.
Jaziri, A., Künzel, E., & Ramesh, V. (2024). Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion. arXiv preprint arXiv:2408.09838.
Zhang, Y., Deekshith, U., Wang, J., & Boedecker, J. (2024, May). Improving the Efficiency and Efficacy of Multi-Agent Reinforcement Learning on Complex Railway Networks with a Local-Critic Approach. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 34, pp. 698-706).
Venugopal, A., Milani, S., Fang, F., & Ravindran, B. (2024, May). MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning. In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (pp. 1865-1873).
Rousseau, T., Amokrane, K., Meddeb, M., Renoir, N. , Brunat, M. , Fort, M. , Schott, L. , Mahler, S. , Berthou, H. (2024, April) Cooperation between a human traffic manager and an AI assistant for an improved railway infrastructure resilience. In Transport Research Arena (TRA2024).
Zhang, Y., Deekshith, U., Wang, J., & Boedecker, J. (2024, March). LCPPO: An Efficient Multi-agent Reinforcement Learning Algorithm on Complex Railway Network. In 34th International Conference on Automated Planning and Scheduling.
Sankaranarayanasamy, M., & Vennelakanti, R. (2024, February). Multi-agent reinforcement learning for shared resource scheduling conflict resolution. In 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-6). IEEE.
Agarwal, S. Exploring Complex Group Dynamics: Visual Analysis of Overlapping Groups and Interactions Over Time. Doctoral dissertation, Dissertation, Duisburg, Essen, Universität Duisburg-Essen, 2024.
Chen, Z., Li, J., Harabor, D., & Stuckey, P. J. (2023). Scalable Rail Planning and Replanning with Soft Deadlines. arXiv preprint arXiv:2306.06455.
Krause, C., Agarwal, S., Burch, M., & Beck, F. (2023). Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison. In Computer Graphics Forum.
Formanek, C., Jeewa, A., Shock, J., & Pretorius, A. (2023). Off-the-Grid MARL: Datasets and Baselines for Offline Multi-Agent Reinforcement Learning. In Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems (pp. 2442-2444).
Nygren, E., Eichenberger, C., & Frejinger, E. (2023). Scope Restriction for Scalable Real-Time Railway Rescheduling: An Exploratory Study. arXiv preprint, arXiv:2305.03574.
Venugopal, A., Milani, S., Fang, F., & Ravindran, B. (2023, Apr 12). Bi-level Latent Variable Model for Sample-Efficient Multi-Agent Reinforcement Learning. arXiv preprint, arXiv:2304.06011.
Song S. ,Na K. -I. and Yu W. (2023.), "Anytime Lifelong Multi-Agent Pathfinding in Topological Maps," in IEEE Access, vol. 11, pp. 20365-20380, 2023, doi: 10. 1109/ACCESS.2023.3249471.
Formanek, C., Jeewa, A., Shock, J., & Pretorius, A. (2023). Off-the-Grid MARL: a Framework for Dataset Generation with Baselines for Cooperative Offline Multi-Agent Reinforcement Learning. arXiv preprint arXiv:2302.00521.
Yakovlev, K. S., Andreychuk, A. A., Skrynnik, A. A., & Panov, A. I. (2023, January). Planning and Learning in Multi-Agent Path Finding. In Doklady Mathematics (pp. 1-6). Moscow: Pleiades Publishing.
Minashina, I. K., Gorbachev, R. A., & Zakharova, E. M. (2023, January). Scheduling in Multiagent Systems Using Reinforcement Learning. In Doklady Mathematics (pp. 1-9). Moscow: Pleiades Publishing.
Fernández López Areal, Mateo (2022) Solving the vehicle rescheduling problem (VRSP) for railroad transport using the deep multi-agent reinforcement learning. Thesis (Master thesis), Biblioteca ETSI Telecomunicación, Universitat Politecnica Madrid.
Dalle, G. (Dec 2022). Machine learning and combinatorial optimization algorithms, with applications to railway planning. Doctoral dissertation, Marne-la-vallée, École des Ponts ParisTech.
Vysušilová, P. (Oct 2022). Awareness and Adaptability in Multi-Agent Reinforcement Learning. Charles University, Faculty of Mathematics and Physics.
Mohapatra, D., Ojha, A., Khadilkar, H., & Ghosh, S. (2022, July). Gatekeeper: A deep reinforcement learning-cum-heuristic based algorithm for scheduling and routing trains in complex environments. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
Svancara, J., & Barták, R. (2022). Tackling Train Routing via Multi-agent Pathfinding and Constraint-based Scheduling. In ICAART (1) (pp. 306-313).
Popescu, T. (2022). Reinforcement learning for train dispatching: A study on the possibility to use reinforcement learning to optimize train ordering and minimize train delays in disrupted situations, inside the rail simulator OSRD.
Jiang, Y., Zhang, K., Li, Q., Chen, J., & Zhu, X. (2022). Multi-Agent Path Finding via Tree LSTM. arXiv preprint arXiv:2210.12933.
Gorsane, R., Mahjoub, O., de Kock, R., Dubb, R., Singh, S., & Pretorius, A. ( 2022). Towards a standardised performance evaluation protocol for cooperative marl. arXiv preprint arXiv:2209.10485.
Leichthammer, L. (2022). Evaluating Planning-based Machine Learning Algorithms for Scheduling Railway Operations.
Chen, Z., Li, J., Harabor, D., Stuckey, P. J., & Koenig, S. (2022, July). Multi-Train Path Finding Revisited. In Proceedings of the International Symposium on Combinatorial Search (Vol. 15, No. 1, pp. 38-46).
Egorov, V., & Shpilman, A. (2022). Scalable Multi-Agent Model-Based Reinforcement Learning. arXiv preprint arXiv:2205.15023.
Schmidt, L. M., Brosig, J., Plinge, A., Eskofier, B. M., & Mutschler, C. (2022, October). An introduction to multi-agent reinforcement learning and review of its application to autonomous mobility. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 1342-1349). IEEE.
Fronda, L., Berta, R., Cesario, P., De Gloria, A., & Bellotti, F. (2022). Modeling the Line Interruption Issue in a Railway Network. In Applications in Electronics Pervading Industry, Environment and Society: APPLEPIES 2021 (pp. 249-255). Cham: Springer International Publishing.
Lövétei, I., Kővári, B., Bécsi, T., & Aradi, S. (2022). Environment Representations of Railway Infrastructure for Reinforcement Learning-Based Traffic Control. Applied Sciences, 12(9), 4465.
Kopacz, A., Mester, Á., Kolumbán, S., & Csató, L. (2022, March). Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem. In 2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000103-000108). IEEE.
Dalle, G., & Parmentier, A. (2022, February). Learning to Solve Stochastic Multi-Agent Path Finding. In 23ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision.
Agarwal, S., Wallner, G., Watson, J., & Beck, F. (2022, April). Spatio-temporal Analysis of Multi-agent Scheduling Behaviors on Fixed-track Networks. In 2022 IEEE 15th Pacific Visualization Symposium (PacificVis) (pp. 21-30). IEEE.
Badyal, S. (2021). A Comparative Study of Multi-Agent Reinforcement Learning on Real World Problems (Doctoral dissertation, Arizona State University).
Leamer, A., Sharma, R., & Khan, M. S. (2021) Multi-Agent Reinforcement Learning on Trains using Classical AI Search Technique.
Chen, Z., Harabor, D. D., Li, J., & Stuckey, P. J. (2021, May). Symmetry breaking for k-robust multi-agent path finding. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 14, pp. 12267-12274).
Hájek, R., Malecek, L., Melecký, M., & Schovancová, J. (2021). Flatland Challenge, Team Learn.
Choi, B., & Kim, J. K. (2021). The Analysis of Flatland Challenge Winners' Multi-agent Methodologies. In Proceedings of the Korea Information Processing Society Conference (pp. 369-372). Korea Information Processing Society.
Li, J., Chen, Z., Zheng, Y., Chan, S. H., Harabor, D., Stuckey, P. J., Hang M., Koenig, S. (2021, May). Scalable rail planning and replanning: Winning the 2020 flatland challenge. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 31, pp. 477-485).
Laurent, F., Schneider, M., Scheller, C., Watson, J.,
Li, J., Chen, Z., Zheng, Y., Chan, S., Makhnev, K.,
Svidchenko, O., Egorov, V., Ivanov, D., Shpilman, A.,
Spirovska, E., Tanevski, O., Nikov, A., Grunder, R., Galevski, D.,
Mitrovski, J., Sartoretti, G., Luo, Z., Damani, M., Bhattacharya, N.,
Agarwal, S., Egli, A., Nygren, E., Mohanty, S. (2021, August).
Flatland competition 2020: MAPF and MARL for efficient train coordination on
a grid world.
In NeurIPS 2020 Competition and Demonstration Track (pp. 275-301). PMLR.
Wälter, J., Mehta, F. D., & Rao, X. (2020). Aiding vehicle scheduling and rescheduling using machine learning. International Journal of Transport Development and Integration, 4(4), 308-320.
Filip, R. (2020). Multi-agent Path-finding for trains with breakdowns.. Czech Technical University in Prague. Faculty of Electrical Engineering.
Mohanty, S., Nygren, E., Laurent, F., Schneider, M., Scheller, C., Bhattacharya, N., Watson, J., Egli, A., Eichenberger, C., Baumberger, C., Vienken, C., Sturm, I., Sartoretti, G., Spigler, G. (2020). Flatland-rl: Multi-agent reinforcement learning on trains. arXiv preprint arXiv:2012.05893.
Roost, D., Meier, R., Huschauer, S., Nygren, E., Egli, A., Weiler, A., & Stadelmann, T. (2020, June). Improving sample efficiency and multi-agent communication in RL-based train rescheduling. In 2020 7th Swiss Conference on Data Science (SDS) (pp. 63-64). IEEE.
Wälter, J. (2020). Existing and novel approaches to the vehicle rescheduling problem (VRSP) (Doctoral dissertation, HSR Hochschule für Technik Rapperswil).
Andreica, M. I. (2020). Winning Solution of the AIcrowd SBB Flatland Challenge 2019-2020. arXiv preprint arXiv:2111.07876.
Meier, R., Roost D. (2019). Existing and novel approaches to the vehicle rescheduling problem (VRSP). Project thesis, Computer Science, Zurich University of Applied Sciences.
Cantini, G. (2019). FLATLAND: A study of Deep Reinforcement Learning methods applied to the vehicle rescheduling problem in a railway environment. Faculty of Science, Computer Science, University of Bologna
Nygren, E., Egli, A., Spigler, G., Ljungström, M., Watson, J., Eichenberger, C., Mollard, G., Mohanty, S. (2019). Flatland challenge: Multi agent reinforcement learning on trains..
Mohanty, S. Nygren, E., Egli, A., Salathé, M. (2019). Flatland-RL : Multi Agent Reinforcement Learning on Trains..