[1] Wang G and Yu Z, A Pontryagin’s maximum principle for non-zero sum differential games of BSDEs with applications, IEEE Transactions on Automatic Control, 2010, 55(7): 1742-1747.
[2] Vamvoudakis K G and Lewis F L, Multi-player non-zero-sum games: Online adaptive learning solution of coupled Hamilton-Jacobi equations, Automatica, 2011, 47(8): 1556-1569.
[3] Powell W B, Approximate Dynamic Programming: Solving the Curse of Dimensionality, Wiley, New York, 2004.
[4] Jiang Y and Jiang Z-P, Robust Adaptive Dynamic Programming, Wiley, Hoboken, 2017.
[5] Sutton R S and Barto A G, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 2018.
[6] Jiang Y and Jiang Z-P, Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics, Automatica, 2012, 48(10): 2699-2704.
[7] Pang B, Bian T, and Jiang Z P, Robust policy iteration for continuous-time linear quadratic regulation, IEEE Transactions on Automatic Control, 2021, 67(1): 504-511.
[8] Rizvi S A A and Lin Z, Reinforcement learning-based linear quadratic regulation of continuoustime systems using dynamic output feedback, IEEE Transactions on Cybernetics, 2020, 50(11): 4670-4679.
[9] Jiang Z P and Jiang Y, Robust adaptive dynamic programming for linear and nonlinear systems: An overview, European Journal of Control, 2013, 19(5): 417-425.
[10] Xie K, Zheng Y, Jiang Y, et al., Optimal dynamic output feedback control of unknown linear continuous-time systems by adaptive dynamic programming, Automatica, 2024, 163: 111601.
[11] Chen C, Xie L, Xie K, et al., Adaptive optimal output tracking of continuous-time systems via output-feedback-based reinforcement learning, Automatica, 2022, 146: 110581.
[12] Chen C, Xie L, Jiang Y, et al., Robust output regulation and reinforcement learning-based output tracking design for unknown linear discrete-time systems, IEEE Transactions on Automatic Control, 2023, 68(4): 2391-2398.
[13] Chen C, Lewis F L, Xie K, et al., Distributed output data-driven optimal robust synchronization of heterogeneous multi-agent systems, Automatica, 2023, 153: 111030.
[14] Chen C, Modares H, Xie K, et al., Reinforcement learning-based adaptive optimal exponential tracking control of linear systems with unknown dynamics, IEEE Transactions on Automatic control, 2019, 64(11): 4423-4438.
[15] Gao W and Jiang Z-P, Adaptive dynamic programming and adaptive optimal output regulation of linear systems, IEEE Transactions on Automatic Control, 2016, 61(12): 4164-4169.
[16] Gao W, Jiang Z-P, and Lewis F L, Leader-to-Formation stability of multi-agent systems: An adaptive optimal control approach, IEEE Transactions on Automatic Control, 2018, 63(10): 3581-3588.
[17] Gao W, Mynuddin M, Wunsch D C, et al., Reinforcement learning-based cooperative optimal output regulation via distributed adaptive internal model, IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(10): 5229-5240.
[18] Xie K, Yu X, and Lan W, Optimal output regulation for unknown continuous-time linear systems by internal model and adaptive dynamic programming, Automatica, 2022, 146: 110564.
[19] Jiang Y, Gao W, Wu J, et al., Reinforcement learning and cooperative H∞ output regulation of linear continuous-time multi-agent systems, Automatica, 2023, 148: 110768.
[20] Dong Y, Gao W, and Jiang Z-P, New results in cooperative adaptive optimal output regulation, Journal of Systems Science & Complexity, 2024, 37(1): 253-272.
[21] Lewis F L, Vrabie D L, and Syrmos V L, Optimal Control, John Wiley & Sons, Inc., Hoboken, 2012.
[22] Bian T and Jiang Z-P, Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design, Automatica, 2016, 71: 348-360.
[23] Vamvoudakis K G, Non-zero sum Nash Q-learning for unknown deterministic continuous-time linear systems, Automatica, 2015, 61: 274-281.
[24] Song R, Wei Q, Zhang H, et al., Discrete-time non-zero-sum games with completely unknown dynamics, IEEE Transactions on Cybernetics, 2021, 51(6): 2929-2943.
[25] Zhang Z, Xu J, and Fu M, Q-learning for feedback Nash strategy of finite-horizon nonzero-sum difference games, IEEE Transactions on Cybernetics, 2022, 52(9): 9170-9178.
[26] Zhang H, Jiang H, Luo C, et al., Discrete-time nonzero-sum games for multiplayer using policyiteration-based adaptive dynamic programming algorithms, IEEE Transactions on Cybernetics, 2017, 47(10): 3331-3340.
[27] Xin X, Tu Y, Stojanovic V, et al., Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems, Applied Mathematics and Computation, 2022, 412: 126537.
[28] Wu S, Linear-quadratic non-zero sum backward stochastic differential game with overlapping information, IEEE Transactions on Automatic Control, 2022, 68(3): 1800-1806.
[29] Zhang B, Wang B, and Cao Y, An online Q-learning method for linear-quadratic nonzero-sum stochastic differential games with completely unknown dynamics, Journal of Systems Science & Complexity, 2024, 37(5): 1907-1922.
[30] Wei Q, Zhu L, Song R, et al., Model-free adaptive optimal control for unknown nonlinear multiplayer nonzero-sum game, IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 879-892.
[31] Jiang H and Zhang H, Iterative ADP learning algorithms for discrete-time multi-player games, Artificial Intelligence Review, 2018, 50: 75-91.
[32] Huo Y, Wang D, Qiao J, et al., Off-policy model-free learning for multi-player non-zero-sum games with constrained inputs, IEEE Transactions on Circuits and Systems I: Regular Papers, 2022, 70(2): 910-920.
[33] Wang Z, Wei Q, and Liu D, Event-triggered adaptive dynamic programming for discrete-time multi-player games, Information Sciences, 2020, 506: 457-470.
[34] Chen C, Xie L, Xie K, et al., Learning the continuous-time optimal decision law from discretetime rewards, National Science Open, 2024, 3(5): 20230054.
[35] Wen Y, Zhang H, Su H, et al., Optimal tracking control for non-zero-sum games of linear discretetime systems via off-policy reinforcement learning, Optimal Control Applications and Methods, 2020, 41(4): 1233-1250.
[36] Zhou P, Xue H, Wen J, et al., Model-free optimal tracking policies for Markov jump systems by solving non-zero-sum games, Information Sciences, 2023, 647: 119423.
[37] Qian Y, Liu M, Wan Y, et al., Distributed adaptive Nash equilibrium solution for differential graphical games, IEEE Transactions on Cybernetics, 2023, 53(4): 2275-2287.
[38] Odekunle A, Gao W, Davari M, et al., Reinforcement learning and non-zero-sum game output regulation for multi-player linear uncertain systems, Automatica, 2020, 112: 108672.
[39] Chen C, Lewis F L, and Li B, Homotopic policy iteration-based learning design for unknown linear continuous-time systems, Automatica, 2022, 138: 110153.