1. Bargiacchi, E., Verstraeten, T., Roijersk, D.M., Nowé, A., van Hasselt, H.: Learning to coordinate with coordination graphs in repeated single-stage multi-agent decision problems. In: The 35th International Conference on Machine Learning, vol. 80, 482–490 (2018)
2. Chen, L., et al.: Multiagent path finding using deep reinforcement learning coupled with hot supervision contrastive loss. IEEE Trans. Industr. Electron. 70(7), 7032–7040 (2023). https://doi.org/10.1109/TIE.2022.3206745
3. Ding, S., Aoyama, H., Lin, D.: Combining multiagent reinforcement learning and search method for drone delivery on a non-grid graph. In: Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection: 20th International Conference, PAAMS 2022, L’Aquila, Italy, July 13–15, 2022, Proceedings, pp. 112–126. Springer-Verlag, Berlin, Heidelberg (2022)
4. Du, Y., et al.: Learning correlated communication topology in multi-agent reinforcement learning. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, pp. 456–464. AAMAS ’21, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2021)
5. Grefenstette, J.J.: Credit assignment in rule discovery systems based on genetic algorithms. Mach. Learn. 3(2), 225–245 (1988). https://doi.org/10.1023/A:1022614421909