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A Brief Review on Mean Field Optimal Control Problem from a Linear Quadratic Perspective

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Abstract

This paper reviews the mean field social (MFS) optimal control problem for multi-agent dynamic systems and the mean-field-type (MFT) optimal control problem for single-agent dynamic systems within the linear quadratic (LQ) framework. For the MFS control problem, this review discusses the existing conclusions on optimization in dynamic systems affected by both additive and multiplicative noises. In exploring MFT optimization, the authors first revisit researches associated with single-player systems constrained by these dynamics. The authors then extend the proposed review to scenarios that include multiple players engaged in Nash games, Stackelberg games, and cooperative Pareto games. Finally, the paper concludes by emphasizing future research on intelligent algorithms for mean field optimization, particularly using reinforcement learning method to design strategies for models with unknown parameters.

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References

  1. Lasry J M and Lions P L, Mean field games, Japanese Journal of Mathematics, 2007, 2): 229–260.

    Article  MathSciNet  MATH  Google Scholar 

  2. Huang M, Large-population LQG games involving a major player: The Nash certainty equivalence principle, SIAM Journal on Control and Optimization, 2010, 48(5): 3318–3353.

    Article  MathSciNet  MATH  Google Scholar 

  3. Huang M, Caines P E, and Malhamé R P, Large-population cost-coupled LQG problems with nonuniform agents: Individual-mass behavior and decentralized ε-nash equilibria, IEEE Transactions on Automatic Control, 2007, 52(9): 1560–1571.

    Article  MathSciNet  MATH  Google Scholar 

  4. Huang M, Caines P E, and Malhamé R P, Social optima in mean field LQG control: Centralized and decentralized strategies, IEEE Transactions on Automatic Control, 2012, 57(7): 1736–1751.

    Article  MathSciNet  MATH  Google Scholar 

  5. Andersson D and Djehiche B, A maximum principle for SDE’s of mean-field type, Applied Mathematics & Optimization, 2011, 63(3): 341–356.

    Article  MathSciNet  MATH  Google Scholar 

  6. Yong J, Linear-quadratic optimal control problems for mean-field stochastic differential equations, SIAM Journal on Control and Optimization, 2013, 51(4): 2809–2838.

    Article  MathSciNet  MATH  Google Scholar 

  7. Elliott R, Li X, and Ni Y, Discrete time mean-field stochastic linear-quadratic optimal control problems, Automatica, 2013, 49(11): 3222–3233.

    Article  MathSciNet  MATH  Google Scholar 

  8. Kolokoltsov V N, Li J, and Yang W, Mean field games and nonlinear Markov processes, 2011, arXiv: 1112.3744.

  9. Moon J and Basar T, Linear quadratic risk-sensitive and robust mean field games, IEEE Transactions on Automatic Control, 2016, 62(3): 1062–1077.

    Article  MathSciNet  MATH  Google Scholar 

  10. Tembine H, Zhu Q, and Basar T, Risk-sensitive mean-field stochastic differential games, Proceedings of the 18th IFAC World Congress, 2011, 3222–3227.

    MATH  Google Scholar 

  11. Hafayed M, A mean-field necessary and sufficient conditions for optimal singular stochastic control, Communications in Mathematical Statistics, 2013, 1(4): 417–435.

    Article  MathSciNet  MATH  Google Scholar 

  12. Jiang X, Wang Y, Zhao D, et al., Online Pareto optimal control of mean-field stochastic multiplayer systems using policy iteration, Science China Information Sciences, 2024, 67(4): 140202.

    Article  MATH  Google Scholar 

  13. Ni Y, Li X, and Zhang J, Indefinite mean-field stochastic linear-quadratic optimal control: From finite horizon to infinite horizon, IEEE Transactions on Automatic Control, 2015, 61(11): 3269–3284.

    Article  MathSciNet  MATH  Google Scholar 

  14. Yang C, Li J, Semasinghe P, et al., Distributed interference and energy-aware power control for ultra-dense D2D networks: A mean field game, IEEE Transactions on Wireless Communications, 2017, 16(2): 1205–1217.

    Article  MATH  Google Scholar 

  15. Couillet R, Perlaza S M, Tembine H, et al., Electric vehicles in the smart grid: A mean field game analysis, IEEE Journal on Selected Areas in Communications, 2012, 30(6): 1086–1096.

    Article  MATH  Google Scholar 

  16. Ma Z, Callaway D S, and Hiskens I A, Decentralized charging control for large populations of plug-in electric vehicles, IEEE Transactions on Control Systems Technology, 2011, 21(1): 67–78.

    Article  MATH  Google Scholar 

  17. Lin R, Xu Z, Huang X, et al., Optimal scheduling management of the parking lot and decentralized charging of electric vehicles based on mean field game, Applied Energy, 2022, 328): 120198.

    Article  MATH  Google Scholar 

  18. Banez R A, Tembine H, Li L, et al., Mean-field-type game-based computation offloading in multi-access edge computing networks, IEEE Transactions on Wireless Communications, 2020, 19(12): 8366–8381.

    Article  MATH  Google Scholar 

  19. Wang Y, Yu F R, Tang H, et al., A mean field game theoretic approach for security enhancements in mobile ad hoc networks, IEEE Transactions on Wireless Communications, 2014, 13(3): 1616–1627.

    Article  MATH  Google Scholar 

  20. Ge X, Jia H, Zhong Y, et al., Energy efficient optimization of wireless-powered 5G full duplex cellular networks: A mean field game approach, IEEE Transactions on Green Communications and Networking, 2019, 3(2): 455–467.

    Article  MATH  Google Scholar 

  21. Guo J, Guo Q, Mou C, et al., A mean field game model of staking system, Digital Finance, 2024, DOI: https://doi.org/10.1007/s42521024001134.

  22. Wang B and Huang M, Mean field production output control with sticky prices: Nash and social solutions, Automatica, 2019, 100): 90–98.

    Article  MathSciNet  MATH  Google Scholar 

  23. Bauso D, Tembine H, and Basar T, Opinion dynamics in social networks through mean-field games, SIAM Journal on Control and Optimization, 2016, 54(6): 3225–3257.

    Article  MathSciNet  MATH  Google Scholar 

  24. Bauso D, Zhang X, and Papachristodoulou A, Density flow in dynamical networks via mean-field games, IEEE Transactions on Automatic Control, 2016, 62(3): 1342–1355.

    Article  MathSciNet  MATH  Google Scholar 

  25. Bauch C T and Earn D J, Vaccination and the theory of games, Proceedings of the National Academy of Sciences, 2004, 101(36): 13391–13394.

    Article  MathSciNet  MATH  Google Scholar 

  26. Carmona R and Delarue F, Probabilistic analysis of mean-field games, SIAM Journal on Control and Optimization, 2013, 51(4): 2705–2734.

    Article  MathSciNet  MATH  Google Scholar 

  27. Dong B, Nie T, and Wu Z, Maximum principle for discrete-time stochastic control problem of mean-field type, Automatica, 2022, 144): 110497.

    Article  MathSciNet  MATH  Google Scholar 

  28. Nuño G and Moll B, Social optima in economies with heterogeneous agents, Review of Economic Dynamics, 2018, 28): 150–180.

    Article  MATH  Google Scholar 

  29. Wang G and Wu Z, A maximum principle for mean-field stochastic control system with noisy observation, Automatica, 2022, 137): 110135.

    Article  MathSciNet  MATH  Google Scholar 

  30. Huang M, Li T, and Zhang J, Stochastic approximation based consensus dynamics over Markovian networks, SIAM Journal on Control and Optimization, 2015, 53(6): 3339–3363.

    Article  MathSciNet  MATH  Google Scholar 

  31. Moon J, Risk-sensitive maximum principle for stochastic optimal control of mean-field type Markov regime-switching jump-diffusion systems, International Journal of Robust and Nonlinear Control, 2021, 31(6): 2141–2167.

    Article  MathSciNet  MATH  Google Scholar 

  32. Weintraub G Y, Benkard C L, and Van Roy B, Markov perfect industry dynamics with many firms, Econometrica, 2008, 76(6): 1375–1411.

    Article  MathSciNet  MATH  Google Scholar 

  33. Bensoussan A, Frehse J, and Yam P, Mean Field Games and Mean Field Type Control Theory, Springer, New York, 2013.

    Book  MATH  Google Scholar 

  34. Caines P E, Huang M, and Malhame R P, Mean field games, Handbook of Dynamic Game Theory, Eds. by Basar T and Zaccour G, Springer, Berlin, 2017.

    MATH  Google Scholar 

  35. Carmona R and Delarue F, Probabilistic Theory of Mean Field Games with Applications, III, Springer Nature, Berlin, 2018.

    Book  MATH  Google Scholar 

  36. Gomes D A and Saude J, Mean field games models — A brief survey, Dynamic Games and Applications, 2014, 4(2): 110–154.

    Article  MathSciNet  MATH  Google Scholar 

  37. Guéant O, Lasry J M, and Lions P L, Mean field games and applications, Paris-Princeton Lectures on Mathematical Finance, Springer, Berlin, 2011.

    MATH  Google Scholar 

  38. Ho Y C, Team decision theory and information structures, Proceedings of the IEEE, 1980, 68(6): 644–654.

    Article  MATH  Google Scholar 

  39. Radner R, Team decision problems, Annals of Mathematical Statistics, 1962, 33(3): 857–881.

    Article  MathSciNet  MATH  Google Scholar 

  40. Wang B, Zhang H, and Zhang J, Mean field linear-quadratic control: Uniform stabilization and social optimality, Automatica, 2020, 121): 109088.

    Article  MathSciNet  MATH  Google Scholar 

  41. Chen X and Huang M, Linear-quadratic mean field control: The invariant subspace method, Automatica, 2019, 107): 582–586.

    Article  MathSciNet  MATH  Google Scholar 

  42. Wang B and Zhang J, Mean field games for large-population multiagent systems with Markov jump parameters, SIAM Journal on Control and Optimization, 2012, 50(4): 2308–2334.

    Article  MathSciNet  MATH  Google Scholar 

  43. Li T and Zhang J, Decentralized tracking-type games for multi-agent systems with coupled ARX models: Asymptotic Nash equilibria, Automatica, 2008, 44(3): 713–725.

    Article  MathSciNet  MATH  Google Scholar 

  44. Wang B, Huang J, and Zhang J, Social optima in robust mean field LQG control: From finite to infinite horizon, IEEE Transactions on Automatic Control, 2020, 66(4): 1529–1544.

    Article  MathSciNet  MATH  Google Scholar 

  45. Wang B and Liang Y, Robust mean field social control problems with applications in analysis of opinion dynamics, International Journal of Control, 2022, 95(12): 3309–3325.

    Article  MathSciNet  MATH  Google Scholar 

  46. Wang B and Zhang J, Social optima in mean field linear-quadratic-Gaussian models with Markov jump parameters, SIAM Journal on Control and Optimization, 2017, 55(1): 429–456.

    Article  MathSciNet  MATH  Google Scholar 

  47. Huang M and Nguyen S L, Linear-quadratic mean field social optimization with a major player, 2019, arXiv: 1904.03346.

  48. Arabneydi J and Aghdam A G, Deep teams: Decentralized decision making with finite and infinite number of agents, IEEE Transactions on Automatic Control, 2020, 65(10): 4230–4245.

    Article  MathSciNet  MATH  Google Scholar 

  49. Arabneydi J and Mahajan A, Linear quadratic mean field teams: Optimal and approximately optimal decentralized solutions, 2016, arXiv: 1609.00056.

  50. Sen N, Huang M, and Malhamé R P, Mean field social control with decentralized strategies and optimality characterization, 2016 IEEE 55th Conference on Decision and Control, 2016, 6056–6061.

    Chapter  MATH  Google Scholar 

  51. Salhab R, Le Ny J, and Malhamé R P, Dynamic collective choice: Social optima, IEEE Transactions on Automatic Control, 2018, 63(10): 3487–3494.

    Article  MathSciNet  MATH  Google Scholar 

  52. Albi G, Choi Y P, Fornasier M, et al., Mean field control hierarchy, Applied Mathematics & Optimization, 2017, 76(1): 93–135.

    Article  MathSciNet  MATH  Google Scholar 

  53. Fornasier M and Solombrino F, Mean-field optimal control, ESAIM: Control, Optimisation and Calculus of Variations, 2014, 20(4): 1123–1152.

    MathSciNet  MATH  Google Scholar 

  54. Kac M, Foundations of kinetic theory, Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Volume III. University of California Press, California, 1956.

    MATH  Google Scholar 

  55. McKean Jr H P, A class of Markov processes associated with nonlinear parabolic equations, Proceedings of the National Academy of Sciences of the USA, 1966, 56(6): 1907–1911.

    Article  MathSciNet  MATH  Google Scholar 

  56. Ahmed N U and Ding X, Controlled McKean-Vlasov equations, Communications in Applied Analysis, 2001, 5(2): 183–206.

    MathSciNet  MATH  Google Scholar 

  57. Buckdahn R, Li J, and Peng S, Mean-field backward stochastic differential equations and related partial differential equations, Stochastic Processes and Applications, 2009, 119(10): 3133–3154.

    Article  MathSciNet  MATH  Google Scholar 

  58. Crisan D and Xiong J, Approximate McKean-Vlasov representations for a class of SPDEs, Stochastics, 2010, 82(1): 53–68.

    Article  MathSciNet  MATH  Google Scholar 

  59. Dawson D A, Critical dynamics and fluctuations for a mean-field model of cooperative behavior, Journal of Statistical Physics, 1983, 31): 29–85.

    Article  MathSciNet  MATH  Google Scholar 

  60. Graham C, McKean-Vlasov Itô-Skorohod equations, and nonlinear diffusions with discrete jump sets, Stochastic Processes and Applications, 1992, 40(1): 69–82.

    Article  MathSciNet  MATH  Google Scholar 

  61. Meyer-Brandis T, Øksendal B, and Zhou X, A mean-field stochastic maximum principle via Malliavin calculus, Stochastics, 2012, 84): 643–666.

    Article  MathSciNet  MATH  Google Scholar 

  62. Huang J, Li X, and Yong J, A linear-quadratic optimal control problem for mean-field stochastic differential equations in infinite horizon, Mathematical Control and Related Fields, 2015, 5(1): 97–139.

    Article  MathSciNet  MATH  Google Scholar 

  63. Ni Y, Elliott R, and Li X, Discrete-time mean-field stochastic linear-quadratic optimal control problems, II: Infinite horizon case, Automatica, 2015, 57): 65–77.

    Article  MathSciNet  MATH  Google Scholar 

  64. Huang J, Wang S, and Wu Z, Backward mean-field linear-quadratic-Gaussian (LQG) games: Full and partial information, IEEE Transactions on Automatic Control, 2016, 61(12): 3784–3796.

    Article  MathSciNet  MATH  Google Scholar 

  65. Wang G, Zhang C, and Zhang W, Stochastic maximum principle for mean-field type optimal control under partial information, IEEE Transactions on Automatic Control, 2013, 59(2): 522–528.

    Article  MathSciNet  MATH  Google Scholar 

  66. Hafayed M and Abbas S, On near-optimal mean-field stochastic singular controls: Necessary and sufficient conditions for near-optimality, Journal of Optimization Theory and Applications, 2014, 160): 778–808.

    Article  MathSciNet  MATH  Google Scholar 

  67. Yong J, Linear-quadratic optimal control problems for mean-field stochastic differential equations: Time consistent solutions, Transactions of the American Mathematical Society, 2017, 369(8): 5467–5523.

    Article  MathSciNet  MATH  Google Scholar 

  68. Barreiro-Gomez J, Duncan T E, and Tembine H, Linear-quadratic mean-field-type games: Jump-diffusion process with regime switching, IEEE Transactions on Automatic Control, 2019, 64(10): 4329–4336.

    Article  MathSciNet  MATH  Google Scholar 

  69. Graber P J, Linear quadratic mean field type control and mean field games with common noise, with application to production of an exhaustible resource, Applied Mathematics and Optimization, 2016, 74): 459–486.

    Article  MathSciNet  MATH  Google Scholar 

  70. Qi Q, Xie L, and Zhang H, Linear-quadratic optimal control for discrete-time mean-field systems with input delay, IEEE Transactions on Automatic Control, 2021, 67(8): 4161–4167.

    MathSciNet  MATH  Google Scholar 

  71. Zhang W, Xie L, and Chen B S, Stochastic H2/HControl: A Nash Game Approach, CRC Press, Florida, 2017.

    Book  MATH  Google Scholar 

  72. Zames G, Feedback and optimal sensitivity: Model reference transformations, multiplicative seminorms, and approximate inverse, IEEE Transactions on Automatic Control, 1981, 26(2): 301–320.

    Article  MathSciNet  MATH  Google Scholar 

  73. Hinrichsen D and Pritchard A J, Stochastic H, SIAM Journal on Control and Optimization, 1998, 36(5): 1504–1538.

    Article  MathSciNet  MATH  Google Scholar 

  74. Zhang W, Zhong S, and Jiang X, Finite-time annular domain stability and asynchronous H control for stochastic switching Markov jump systems, IEEE Transactions on Automatic Control, 2024, DOI: https://doi.org/10.1109/TAC.2024.3379248.

  75. Chen B S and Zhang W, Stochastic H2/H control with state-dependent noise, IEEE Transactions on Automatic Control, 2004, 49(1): 45–57.

    Article  MathSciNet  MATH  Google Scholar 

  76. Hou T, Zhang W, and Ma H, Finite horizon H2/H control for discrete-time stochastic systems with Markovian jumps and multiplicative noise, IEEE Transactions on Automatic Control, 2010, 55(5): 1185–1191.

    Article  MathSciNet  MATH  Google Scholar 

  77. Ma H, Mou C, and Ho D W, Open-loop H2/H control for discrete-time mean-field stochastic systems, IEEE Transactions on Automatic Control, 2024, DOI: https://doi.org/10.1109/TAC.2024.3395018.

  78. Ma H, Zhang W, and Hou T, Infinite horizon H2/H control for discrete-time time-varying Markovian jump systems with multiplicative noise, Automatica, 2012, 48(7): 1447–1454.

    Article  MathSciNet  MATH  Google Scholar 

  79. Ma L and Zhang W, Output feedback H control for discrete-time mean-field stochastic systems, Asian Journal of Control, 2015, 17(6): 2241–2251.

    Article  MathSciNet  MATH  Google Scholar 

  80. Ma L, Zhang T, Zhang W, et al., Finite horizon mean-field stochastic H2/H control for continuous-time systems with (x, v)-dependent noise, Journal of the Franklin Institute, 2015, 352): 5393–5414.

    Article  MATH  Google Scholar 

  81. Zhang W, Huang Y, and Zhang H, Stochastic H2/H control for discrete-time systems with state and disturbance dependent noise, Automatica, 2007, 43(3): 513–521.

    Article  MathSciNet  MATH  Google Scholar 

  82. Zhang W, Huang Y, and Xie L, Infinite horizon stochastic H2/H control for discrete-time systems with state and disturbance dependent noise, Automatica, 2008, 44(9): 2306–2316.

    Article  MathSciNet  MATH  Google Scholar 

  83. Abou-Kandil H and Bertrand P, Analytical solution for an open-loop Stackelberg game, IEEE Transactions on Automatic Control, 1985, 30(12): 1222–1224.

    Article  MathSciNet  MATH  Google Scholar 

  84. Yong J, A leader-follower stochastic linear quadratic differential game, SIAM Journal on Control and Optimization, 2002, 41(4): 1015–1041.

    Article  MathSciNet  MATH  Google Scholar 

  85. Mukaidani H and Xu H, Infinite horizon linear-quadratic Stackelberg games for discrete-time stochastic systems, Automatica, 2017, 76): 301–308.

    Article  MathSciNet  MATH  Google Scholar 

  86. Xu J and Zhang H, Sufficient and necessary open-loop Stackelberg strategy for two-player game with time delay, IEEE Transactions on Cybernetics, 2015, 46(2): 438–449.

    Article  MATH  Google Scholar 

  87. Moon J, Linear-quadratic stochastic Stackelberg differential games for jump-diffusion systems, SIAM Journal on Control and Optimization, 2021, 59(2): 954–976.

    Article  MathSciNet  MATH  Google Scholar 

  88. Aberkane S and Dragan V, An addendum to the problem of zero-sum LQ stochastic mean-field dynamic games, Automatica, 2023, 153): 111007.

    Article  MathSciNet  MATH  Google Scholar 

  89. Lin Y, Jiang X, and Zhang W, Necessary and sufficient conditions for Pareto optimality of the stochastic systems in finite horizon, Automatica, 2018, 94): 341–348.

    Article  MathSciNet  MATH  Google Scholar 

  90. Lv S, Xiong J, and Zhang X, Linear quadratic leader-follower stochastic differential games for mean-field switching diffusions, Automatica, 2023, 154): 111072.

    Article  MathSciNet  MATH  Google Scholar 

  91. Moon J and Yang H J, Linear-quadratic time-inconsistent mean-field type Stackelberg differential games: Time-consistent open-loop solutions, IEEE Transactions on Automatic Control, 2020, 66(1): 375–382.

    Article  MathSciNet  MATH  Google Scholar 

  92. Engwerda J, LQ Dynamic Optimization and Differential Games, John Wiley and Sons, Chichester, 2005.

    MATH  Google Scholar 

  93. Engwerda J, The regular convex cooperative linear quadratic control problem, Automatica, 2008, 44(9): 2453–2457.

    Article  MathSciNet  MATH  Google Scholar 

  94. Engwerda J, Necessary and sufficient conditions for Pareto optimal solutions of cooperative differential games, SIAM Journal on Control and Optimization, 2010, 48(6): 3859–3881.

    Article  MathSciNet  MATH  Google Scholar 

  95. Jiang X, Su S, and Zhao D, Pareto optimal strategy under H constraint for the mean-field stochastic systems in infinite horizon, IEEE Transactions on Cybernetics, 2022, 53(11): 6963–6976.

    Article  MATH  Google Scholar 

  96. Lin Y, Necessary/sufficient conditions for Pareto optimality in finite horizon mean-field type stochastic differential game, Automatica, 2020, 119): 108951.

    Article  MathSciNet  MATH  Google Scholar 

  97. Lin Y, Jiang X, and Zhang W, An open-loop Stackelberg strategy for the linear quadratic mean-field stochastic differential game, IEEE Transactions on Automatic Control, 2019, 64(1): 97–110.

    Article  MathSciNet  MATH  Google Scholar 

  98. Lin Y, Zhang T, and Zhang W, Pareto-based guaranteed cost control of the uncertain mean-field stochastic systems in infinite horizon, Automatica, 2018, 92): 197–209.

    Article  MathSciNet  MATH  Google Scholar 

  99. Lin Y and Zhang W, Necessary/sufficient conditions for Pareto optimum in cooperative difference game, Optimal Control Applications and Methods, 2018, 39(2): 1043–1060.

    Article  MathSciNet  MATH  Google Scholar 

  100. Zhang W, Peng C, and Jiang X, Pareto stochastic cooperative games in multiobjective dynamic optimization problems: A survey, Control and Decision, 2023, 38(7): 1789–1801.

    MATH  Google Scholar 

  101. Zhang W and Chen B S, On stabilizability and exact observability of stochastic systems with their applications, Automatica, 2004, 40(1): 87–94.

    Article  MathSciNet  MATH  Google Scholar 

  102. Zhang W, Zhang H, and Chen B S, Generalized Lyapunov equation approach to state-dependent stochastic stabilization/detectability criterion, IEEE Transactions on Automatic Control, 2008, 53(7): 1630–1642.

    Article  MathSciNet  MATH  Google Scholar 

  103. Wang B and Zhang H, Indefinite linear quadratic mean field social control problems with multiplicative noise, IEEE Transactions on Automatic Control, 2021, 66(11): 5221–5236.

    Article  MathSciNet  MATH  Google Scholar 

  104. Zhang W, Ma L, and Zhang T, Discrete-time mean-field stochastic H2/H control, Journal of Systems Science & Complexity, 2017, 30(3): 765–781.

    Article  MathSciNet  MATH  Google Scholar 

  105. Peng C and Zhang W, Pareto optimality in infinite horizon mean-field stochastic cooperative linear-quadratic difference games, IEEE Transactions on Automatic Control, 2023, 68(7): 4113–4126.

    MathSciNet  MATH  Google Scholar 

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Correspondence to Daniel W. C. Ho.

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This research was supported by the National Natural Science Foundation of China under Grant Nos. 62103442, 12326343, 62373229, the Research Grants Council of the Hong Kong Special Administrative Region, China under Grant Nos. CityU 11213023, 11205724, the Natural Science Foundation of Shandong Province under Grant No. ZR2021QF080, the Taishan Scholar Project of Shandong Province under Grant No. tsqn202408110, the Fundamental Research Foundation of the Central Universities under Grant No. 23CX06024A, and the Outstanding Youth Innovation Team in Shandong Higher Education Institutions under Grant No. 2023KJ061.

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Jiang, X., Ho, D.W.C. & Zhang, W. A Brief Review on Mean Field Optimal Control Problem from a Linear Quadratic Perspective. J Syst Sci Complex 38, 390–420 (2025). https://doi.org/10.1007/s11424-025-4501-0

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