[43] Mark Towers, Jordan K. Terry, Ariel Kwiatkowski, John U. Balis, Gianluca de Cola, Tristan Deleu, Manuel Goulão, Andreas Kallinteris, Arjun KG, Markus Kimmel, Rodrigo Perez-Vicente, Andrea Pierre, Sander Schulhoff, Jun Jet Tai, Andrew Tan Jin Shen, and Omar G. Younis. Gymnasium, 2023.
[44] J Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis S Santos, Clemens Dieffendahl, Caroline Horsch, Rodrigo Perez-Vicente, et al. Pettingzoo: Gym for multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34:15032-15043, 2021.
[45] Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica. RLlib: Abstractions for distributed reinforcement learning. Proceedings of the 35th International Conference on Machine Learning, 80:3053-3062, 2018.
[46] Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, and João G.M. Araujo. Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms. Journal of Machine Learning Research, 23(274):1-18, 2022.
[47] Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Zhihui Li, Xiaodan Liang, Xiaojun Chang, and Yaodong Yang. Marlib: A scalable multi-agent reinforcement learning library. arXiv preprint arXiv:2210.13708, 2022.
[48] Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, and Vincent Moens. TorchRL: A data-driven decision-making library for PyTorch. arXiv:2306.00577, 2023.
[49] Rihab Gorsane, Omayma Mahjoub, Ruan John de Kock, Roland Dubb, Siddarth Singh, and Arnu Pretorius. Towards a standardised performance evaluation protocol for cooperative marl. Advances in Neural Information Processing Systems, 35:5510-5521, 2022.
[50] Mathieu Laurière, Sarah Perrin, Matthieu Geist, and Olivier Pietquin. Learning mean field games: A survey. arXiv:2205.12944, 2022.
[51] Ove Ditlevsen and Henrik O. Madsen. Structural reliability methods, volume 178. Wiley New York, 1996.
[52] Nandar Hlaing, Pablo G Morato, Jannie S Nielsen, Peyman Amirafshari, Athanasios Kolios, and Philippe Rigo. Inspection and maintenance planning for offshore wind structural components: integrating fatigue failure criteria with Bayesian networks and Markov decision processes. Structure and Infrastructure Engineering, 18(7):983-1001, 2022.
[53] Inge Lotsberg, Gudfinnur Sigurdsson, Arne Fjeldstad, and Torgeir Moan. Probabilistic methods for planning of inspection for fatigue cracks in offshore structures. *Marine Structures*, 46: 167-192, 2016.
[54] Christopher JCH Watkins and Peter Dayan. Q-learning. Machine learning, 8(3-4):279-292, 1992.
[55] Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi. QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning. Proceedings of the 36th International Conference on Machine Learning, 2019.
[56] David Ha, Andrew Dai, and Quoc V. Le. HyperNetworks. 5th International Conference on Learning Representations, 2016.
[57]Matthia Sabatelli,Gilles Loupe,Pierre Geurts,and Marco A Wiering.Deep quality-value (DQV) learning. Advances in Neural Information Processing Systems, Deep Reinforcement Learning Workshop, 2018.
[58]Matthia Sabatelli,Gilles Louppe,Pierre Geurts,and Marco Wiering.The deep quality-value family of deep reinforcement learning algorithms. In International Joint Conference on Neural Networks (IJCNN),2020.