[1] P. Stone and M. Veloso, “Multiagent systems: A survey from a machine learning perspective,” Autonomous Robots, vol. 8, no. 3, pp. 345–383, 2000.
[2] J. Verbraeken, M. Wolting, J. Katzy, J. Kloppenburg, T. Verbelen, and J. S. Rellermeyer, “A survey on distributed machine learning,” ACM Computing Surveys, vol. 53, no. 2, pp. 1–33, 2020.
[3] P. Luo, H. Xiong, K. Lu, and Z. Shi, "Distributed classification in peer-to-peer networks," in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 968-976, 2007.
[4] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-efficient learning of deep networks from decentralized data," in Artificial Intelligence and Statistics, pp. 1273-1282, 2017.
[5] M. Bendechache and M.-T. Kechadi, "Distributed clustering algorithm for spatial data mining," in IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, pp. 60-65, 2015.
[6] A. Bhaskara and M. Wijewardena, “Distributed clustering via lsh based data partitioning,” in International Conference on Machine Learning, pp. 570–579, 2018.
[7] L. Espeholt, H. Soyer, R. Munos, K. Simonyan, V. Mnih, T. Ward, Y. Doron, V. Firoiu, T. Harley, I. Dunning, et al., "Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures," in International Conference on Machine Learning, pp. 1407–1416, 2018.
[8] Y. Wang, M. Damani, P. Wang, Y. Cao, and G. Sartoretti, "Distributed reinforcement learning for robot teams: A review," Current Robotics Reports, vol. 3, no. 4, pp. 239-257, 2022.
[9] S. Liu and M. Zhu, "Distributed inverse constrained reinforcement learning for multi-agent systems," in Advances in Neural Information Processing Systems, vol. 35, pp. 33444-33456, 2022.
[10] P. Abbeel and A. Y. Ng, "Apprenticeship learning via inverse reinforcement learning," in International Conference on Machine Learning, pp. 1-8, 2004.
[11] B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey, “Maximum entropy inverse reinforcement learning,” in National Conference on Artificial intelligence, vol. 8, pp. 1433–1438, 2008.
[12] S. Zeng, C. Li, A. Garcia, and M. Hong, "Maximum-likelihood inverse reinforcement learning with finite-time guarantees," Advances in Neural Information Processing Systems, vol. 35, pp. 10122-10135, 2022.
[13] S. Zeng, M. Hong, and A. Garcia, "Structural estimation of markov decision processes in high-dimensional state space with finite-time guarantees," arXiv preprint arXiv:2210.01282, 2022.
[14] S. Zeng, C. Li, A. Garcia, and M. Hong, "Understanding expertise through demonstrations: A maximum likelihood framework for offline inverse reinforcement learning," arXiv preprint arXiv:2302.07457, 2023.
[15] T. S. Reddy, V. Gopikrishna, G. Zaruba, and M. Huber, "Inverse reinforcement learning for decentralized non-cooperative multiagent systems," in IEEE International Conference on Systems, Man, and Cybernetics, pp. 1930-1935, 2012.
[16] S. Natarajan, G. Kunapuli, K. Judah, P. Tadepalli, K. Kersting, and J. Shavlik, “Multi-agent inverse reinforcement learning,” in International Conference on Machine Learning and Applications, pp. 395–400, 2010.
[17] L. Yu, J. Song, and S. Ermon, “Multi-agent adversarial inverse reinforcement learning,” in International Conference on Machine Learning, pp. 7194–7201, 2019.