[27] Sharada Mohanty, Erik Nygren, Florian Laurent, Manuel Schneider, Christian Scheller, Nilabha Bhattacharya, Jeremy Watson, Adrian Egli, Christian Eichenberger, Christian Baumberger, et al. Flatland-RL: Multi-agent reinforcement learning on trains. arXiv:2012.05893, 2020.
[28] Huichu Zhang, Siyuan Feng, Chang Liu, Yaoyao Ding, Yichen Zhu, Zihan Zhou, Weinan Zhang, Yong Yu, Haiming Jin, and Zhenhui Li. CityFlow: A multi-agent reinforcement learning environment for large scale city traffic scenario. In The world wide web conference, pages 3620-3624, 2019.
[29] Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, and Stefano V. Albrecht. Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (NeurIPS), 2021.
[30] Filippos Christianos, Lukas Schäfer, and Stefano V. Albrecht. Shared experience actor-critic for multi-agent reinforcement learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, 2020.
[31] Elizabeth Bismut and Daniel Straub. Optimal adaptive inspection and maintenance planning for deteriorating structural systems. Reliability Engineering & System Safety, 215:107891, 2021.
[32] Konstantinos G. Papakonstantinou and Masanobu Shinozuka. Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory. Reliability Engineering & System Safety, 130:202-213, 2014.
[33] Konstantinos G. Papakonstantinou and Masanobu Shinozuka. Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation. Reliability Engineering & System Safety, 130:214-224, 2014.
[34] Charalampos P. Andriotis and Konstantinos G. Papakonstantinou. Managing engineering systems with large state and action spaces through deep reinforcement learning. Reliability Engineering & System Safety, 191:106483, 2019.
[35] Charalampos P. Andriotis and Konstantinos G. Papakonstantinou. Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints. Reliability Engineering & System Safety, 212:107551, 2021.
[36] Pablo G. Morato, Charalampos P. Andriotis, Konstantinos G. Papakonstantinou, and Philippe Rigo. Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through bayesian networks and deep reinforcement learning. Reliability Engineering & System Safety, 235:109144, 2023.
[37] Van-Thai Nguyen, Phuc Do, Alexandre Voisin, and Benoit Lung. Weighted-QMIX-based optimization for maintenance decision-making of multi-component systems. *PHM Society European Conference*, 7(1):360–367, 2022.
[38] Mohammad Saifullah, Charalampos P. Andriotis, Konstantinos G. Papakonstantinou, and Shelley M. Stoffels. Deep reinforcement learning-based life-cycle management of deteriorating transportation systems. In *Bridge Safety, Maintenance, Management, Life-Cycle, Resilience and Sustainability*, pages 293–301. CRC Press, 2022.
[39] Frans A. Oliehoek and Christopher Amato. A concise introduction to decentralized POMDPs, volume 1. Springer, 2016.
[40] Richard E Barlow and Klaus D Heidtmann. Computing k-out-of-n system reliability. IEEE Transactions on Reliability, 33(4):322-323, 1984.
[41] Felipe Giro, Jose Mihael, Pablo G. Morato, and Philippe Rigo. Inspection and maintenance planning for offshore wind support structures: Modelling reliability and inspection costs at the system level. In International Conference on Offshore Mechanics and Arctic Engineering, volume 85864, 2022.
[42] Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. OpenAI Gym. arXiv:1606.01540, 2016.