[18] D. R. Scobee and S. S. Sastry, “Maximum likelihood constraint inference for inverse reinforcement learning,” in International Conference on Learning Representations, 2019.
[19] S. Malik, U. Anwar, A. Aghasi, and A. Ahmed, “Inverse constrained reinforcement learning,” in International Conference on Machine Learning, pp. 7390–7399, 2021.
[20] N. Rhinehart and K. M. Kitani, "First-person activity forecasting with online inverse reinforcement learning," in IEEE International Conference on Computer Vision, pp. 3696-3705, 2017.
[21] S. Arora, P. Doshi, and B. Banerjee, "Online inverse reinforcement learning under occlusion," in International Conference on Autonomous Agents and Multiagent Systems, pp. 1170-1178, 2019.
[22] E. Hazan, K. Singh, and C. Zhang, "Efficient regret minimization in non-convex games," in International Conference on Machine Learning, pp. 1433–1441, 2017.
[23] N. Hallak, P. Mertikopoulos, and V. Cevher, “Regret minimization in stochastic non-convex learning via a proximal-gradient approach,” in International Conference on Machine Learning, pp. 4008–4017, 2021.
[24] N. Das, S. Bechtle, T. Davchev, D. Jayaraman, A. Rai, and F. Meier, "Model-based inverse reinforcement learning from visual demonstrations," in Conference on Robot Learning, pp. 1930-1942, 2021.
[25] S. Xu and M. Zhu, "Efficient gradient approximation method for constrained bilevel optimization," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 10, pp. 12509-12517, 2023.
[26] S. Ghadimi and M. Wang, "Approximation methods for bilevel programming," arXiv preprint arXiv:1802.02246, 2018.
[27] N. Couellan and W. Wang, "On the convergence of stochastic bi-level gradient methods," Optimization, 2016.
[28] K. Ji, J. Yang, and Y. Liang, "Bilevel optimization: Convergence analysis and enhanced design," in International Conference on Machine Learning, pp. 4882-4892, 2021.
[29] M. Hong, H.-T. Wai, Z. Wang, and Z. Yang, "A two-timescale framework for bilevel optimization: Complexity analysis and application to actor-critic," arXiv preprint arXiv:2007.05170, 2020.
[30] T. Chen, Y. Sun, Q. Xiao, and W. Yin, "A single-timescale method for stochastic bilevel optimization," in International Conference on Artificial Intelligence and Statistics, pp. 2466-2488, 2022.
[31] E. Altman and A. Shwartz, “Constrained markov games: Nash equilibria,” in Advances in Dynamic Games and Applications, pp. 213–221, 2000.
[32] D. Yuan, A. Proutiere, and G. Shi, "Distributed online optimization with long-term constraints," IEEE Transactions on Automatic Control, vol. 67, no. 3, pp. 1089-1104, 2021.
[33] L. Wang, Q. Cai, Z. Yang, and Z. Wang, "Neural policy gradient methods: Global optimality and rates of convergence," in International Conference on Learning Representations, 2019.
[34] H. Kumar, A. Koppel, and A. Ribeiro, "On the sample complexity of actor-critic method for reinforcement learning with function approximation," arXiv preprint arXiv:1910.08412, 2019.
[35] K. Zhang, A. Koppel, H. Zhu, and T. Basar, "Global convergence of policy gradient methods to (almost) locally optimal policies," SIAM Journal on Control and Optimization, vol. 58, no. 6, pp. 3586-3612, 2020.
[36] K. Zhang, Z. Yang, H. Liu, T. Zhang, and T. Basar, “Fully decentralized multi-agent reinforcement learning with networked agents,” in International Conference on Machine Learning, pp. 5872–5881, 2018.