[53] Esmaeil Seraj, Andrew Silva, and Matthew Gombolay. Multi-uav planning for cooperative wildfire coverage and tracking with quality-of-service guarantees. Journal of Autonomous Agents and Multi-Agent Systems, 36(2):39, 2022.
[54] Esmaeil Seraj, Zheyuan Wang, Rohan Paleja, Daniel Martin, Matthew Sklar, Anirudh Patel, and Matthew Gombolay. Learning efficient diverse communication for cooperative heterogeneous teaming. In Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pages 1173-1182, 2022.
[55] Esmaeil Seraj, Zheyuan Wang, Rohan Paleja, Matthew Sklar, Anirudh Patel, and Matthew Gombolay. Heterogeneous graph attention networks for learning diverse communication. arXiv preprint arXiv:2108.09568, 2021.
[56] Esmaeil Seraj, Xiyang Wu, and Matthew Gombolay. Firecommander: An interactive, probabilistic multi-agent environment for heterogeneous robot teams. arXiv preprint arXiv:2011.00165, 2020.
[57] Andy Shih, Stefano Ermon, and Dorsa Sadigh. Conditional imitation learning for multi-agent games. In Proceedings of the 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 166-175. IEEE, 2022.
[58] Marco AC Simões, Robson Marinho da Silva, and Tatiane Nogueira. A dataset schema for cooperative learning from demonstration in multi-robot systems. Journal of Intelligent & Robotic Systems, 99:589–608, 2020.
[59] Amanpreet Singh, Tushar Jain, and Sainbayar Sukhbaatar. Learning when to communicate at scale in multiagent cooperative and competitive tasks. arXiv preprint arXiv:1812.09755, 2018.
[60] Jiaming Song, Hongyu Ren, Dorsa Sadigh, and Stefano Ermon. Multi-agent generative adversarial imitation learning. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 31, 2018.
[61] Adrian Šošic, Wasiur R KhudaBukhsh, Abdelhak M Zoubir, and Heinz Koeppl. Inverse reinforcement learning in swarm systems. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems (AAMAS), pages 1413-1421, 2017.
[62] William Squires and Sean Luke. Scalable heterogeneous multiagent learning from demonstration. In Proceedings of the International Conference on Practical Applications of Agents and Multi-Agent Systems, pages 264-277. Springer, 2020.
[63] Aaron Steinfeld, Odest Chadwicke Jenkins, and Brian Scassellati. The oz of wizard: simulating the human for interaction research. In Proceedings of the ACM/IEEE International Conference on Human Robot Interaction (HRI), pages 101-108, 2009.
[64] Sriram Ganapathi Subramanian, Matthew E Taylor, Kate Larson, and Mark Crowley. Multiagent advisor q-learning. Journal of Artificial Intelligence Research, 74:1-74, 2022.
[65] Sainbayar Sukhbaatar, Rob Fergus, et al. Learning multiagent communication with backpropagation. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 29, 2016.
[66] Faraz Torabi, Garrett Warnell, and Peter Stone. Behavioral cloning from observation. arXiv preprint arXiv:1805.01954, 2018.
[67] Albert Tung, Josiah Wong, Ajay Mandlekar, Roberto Martin-Martín, Yuke Zhu, Li Fei-Fei, and Silvio Savarese. Learning multi-arm manipulation through collaborative teleoperation. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 9212–9219. IEEE, 2021.
[68] Hongwei Wang, Lantao Yu, Zhangjie Cao, and Stefano Ermon. Multi-agent imitation learning with copulas. In Machine Learning and Knowledge Discovery in Databases, pages 139-156. Springer, 2021.