[39] S. Leonardos, W. Overman, I. Panageas, and G. Piliouras. Global convergence of multiagent policy gradient in Markov potential games. In International Conference on Learning Representations, 2021.
[40] Q. Liu, T. Yu, Y. Bai, and C. Jin. A sharp analysis of model-based reinforcement learning with self-play. In International Conference on Machine Learning, 2021.
[41] S. Liu, M. Lanctot, L. Marris, and N. Heess. Simplex neural population learning: Any-mixture bayes-optimality in symmetric zero-sum games. In International Conference on Machine Learning, pages 13793-13806. PMLR, 2022.
[42] R. Lowe, Y. I. Wu, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mordatch. Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in Neural Information Processing Systems, 30, 2017.
[43] S. V. Macua, J. Zazo, and S. Zazo. Learning parametric closed-loop policies for Markov potential games. In International Conference on Learning Representations, 2018.
[44] W. Mao and T. Bāsār. Provably efficient reinforcement learning in decentralized general-sum Markov games. Dynamic Games and Applications, 13:165-186, 2023.
[45] W. Mao, K. Zhang, R. Zhu, D. Simchi-Levi, and T. Başar. Near-optimal model-free reinforcement learning in non-stationary episodic MDPs. In International Conference on Machine Learning, pages 7447-7458. PMLR, 2021.
[46] W. Mao, H. Qiu, C. Wang, H. Franke, Z. Kalbarczyk, R. Iyer, and T. Basar. A mean-field game approach to cloud resource management with function approximation. Advances in Neural Information Processing Systems, 35:36243-36258, 2022.
[47] W. Mao, L. Yang, K. Zhang, and T. Bāsār. On improving model-free algorithms for decentralized multi-agent reinforcement learning. In International Conference on Machine Learning, pages 15007-15049, 2022.
[48] D. Nekipelov, V. Syrgkanis, and E. Tardos. Econometrics for learning agents. In Proceedings of the 16th ACM Conference on Economics and Computation, pages 1-18, 2015.
[49] A. Nichol, J. Achiam, and J. Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018.
[50] F. Orabona and D. Pál. Scale-free online learning. Theoretical Computer Science, 716:50-69, 2018.
[51] P. A. Ortega, J. X. Wang, M. Rowland, T. Genewein, Z. Kurth-Nelson, R. Pascanu, N. Heess, J. Veness, A. Pritzel, P. Sprechmann, et al. Meta-learning of sequential strategies. arXiv preprint arXiv:1905.03030, 2019.
[52] S. Perrin, J. Pérolat, M. Laurière, M. Geist, R. Elie, and O. Pietquin. Fictitious play for mean field games: Continuous time analysis and applications. Advances in Neural Information Processing Systems, 33:13199-13213, 2020.
[53] H. Qiu, W. Mao, C. Wang, H. Franke, A. Youssef, Z. T. Kalbarczyk, T. Basar, and R. K. Iyer. _AWARE: Automate workload autoscaling with reinforcement learning in production cloud systems_. In _USENIX Annual Technical Conference_, 2023.
[54] A. Rajeswaran, C. Finn, S. M. Kakade, and S. Levine. Meta-learning with implicit gradients. Advances in Neural Information Processing Systems, 32, 2019.
[55] S. Rakhlin and K. Sridharan. Optimization, learning, and games with predictable sequences. Advances in Neural Information Processing Systems, 26, 2013.
[56] A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap. Meta-learning with memory-augmented neural networks. In International Conference on Machine Learning, pages 1842-1850. PMLR, 2016.