Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takác. Reinforcement learning for solving the vehicle routing problem. Advances in Neural Information Processing Systems, pages 9839-9849, 2018.
JQ James, Wen Yu, and Jiatao Gu. Online vehicle routing with neural combinatorial optimization and deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 20 (10):3806-3817, 2019.
Lu Duan, Yang Zhan, Haoyuan Hu, Yu Gong, Jiangwen Wei, Xiaodong Zhang, and Yinghui Xu. Efficiently solving the practical vehicle routing problem: A novel joint learning approach. *Special Interest Group on Knowledge Discovery in Data*, pages 3054–3063, 2020.
Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin, Andreas Damianou, Pablo Moreno Garcia, Arpit Jain, Runfei Luo, Alvaro Maggiar, Balakrishnan Narayanaswamy, and Chun Ye. Orl: Reinforcement learning benchmarks for online stochastic optimization problems. Association for the Advancement of Artificial Intelligence, 2020.
Christian D Hubbs, Hector D Perez, Owais Sarwar, Nikolaos V Sahinidis, Ignacio E Grossmann, and John M Wassick. OR-gym: A reinforcement learning library for operations research problems. arXiv preprint arXiv:2008.06319, 2020.
Michael L Littman. Markov games as a framework for multi-agent reinforcement learning. Machine Learning, pages 157-163, 1994.
Lloyd S Shapley. Stochastic games. Proceedings of the National Academy of Sciences, 39(10): 1095-1100, 1953.
Craig Boutilier. Planning, learning and coordination in multiagent decision processes. Theoretical Aspects of Rationality and Knowledge, 96:195-210, 1996.
Martin Lauer and Martin Riedmiller. An algorithm for distributed reinforcement learning in cooperative multi-agent systems. International Conference on Machine Learning, 2000.
Yu-Chi Ho. Team decision theory and information structures. Proceedings of the IEEE, 68(6): 644-654, 1980.
Xiaofeng Wang and Tuomas Sandholm. Reinforcement learning to play an optimal nash equilibrium in team markov games. Advances in Neural Information Processing Systems, 15, 2002.
Daniel S Bernstein, Robert Givan, Neil Immerman, and Shlomo Zilberstein. The complexity of decentralized control of markov decision processes. Mathematics of Operations Research, 27(4): 819-840, 2002.
Frans A Oliehoek and Christopher Amato. A Concise Introduction to Decentralized POMDPs. Springer, 2016.
Zheng Wang, Kun Fu, and Jieping Ye. Learning to estimate the travel time. International Conference on Knowledge Discovery & Data Mining, pages 858-866, 2018.
Jean-Michel Lasry and Pierre-Louis Lions. Mean field games. Japanese Journal of Mathematics, 2 (1):229-260, 2007.
Xin Guo, Anran Hu, Renyuan Xu, and Junzi Zhang. Learning mean-field games. Advances in Neural Information Processing Systems, 32, 2019.
Jayakumar Subramanian and Aditya Mahajan. Reinforcement learning in stationary mean-field games. International Conference on Autonomous Agents and Multiagent Systems, pages 251-259, 2019.
Michel Gendreau, Jean-Yves Potvin, et al. Handbook of Metaheuristics, volume 2. Springer, 2010.
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. CoRR, 2017. URL http://arxiv.org/abs/1707.06347.