یادگیری تقویتی معکوس مبتنی بر رویداد برای اجماع رهبر-پیرو بهینه تطبیقی سیستم‌های چندعاملی ناشناخته

[1] K. Deng, Y. Chen, and C. Belta, ‘‘An approximate dynamic programming approach to multiagent persistent monitoring in stochastic environments with temporal logic constraints’’, IEEE Trans. Autom. Control, vol. 62, no. 9, pp. 4549–4563, 2017.

[2] D. Panagou, D. M. Stipanovi¢, and P. G. Voulgaris, ‘‘Distributed coordination control for multi-robot networks using Lyapunov-like barrier functions,’’ IEEE Trans. Autom. Control, vol. 61, no. 3, pp. 617–632, 2015.

[3] J. Long, W. Wang, J. Huang, J. Lu, and K. Liu, “Adaptive leaderless consensus for uncertain high-order nonlinear multiagent systems with event-triggered communication,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 11, pp. 7101–7111, Nov. 2022.

]4[ عرفان شهامت خواه، محمد طباطبایی، «کنترل ردیابی رهبر-پیرو رخداد-تحریک سیستم‌های چندعاملی با دینامیک تک انتگرال‌گیر چندمتغیره»، مجله مهندسی برق دانشگاه تبریز، جلد 50، شماره 1، صفحات 253-267، 1399.

]5[ بابک عبدالملکی، علیرضا سیفی، محمدمهدی عارفی، «کنترل ردیابی رهبر-پیرو رخداد-تحریک سیستم‌های چندعاملی با دینامیک تک انتگرال‌گیر چندمتغیره»، مجله مهندسی برق دانشگاه تبریز، جلد 48، شماره 2، صفحات 777-784، 1397.

[6] X. Liu, J. Sun, L. Dou, and J. Chen, “Leader-following consensus for discrete-time multi-agent systems with parameter uncertainties based on the event-triggered strategy,” Journal of Systems Science and Complexity, vol. 30, no. 1, pp. 30–45, Feb. 2017.

[7] T. Basar and G.J. Olsder, “Dynamic noncooperative game theory,” Society for Industrial and Applied Mathematics, 1998.

[8] R.S. Sutton, and A.G. Barto, “Reinforcement learning: An introduction,” Robotica, vol. 17, no. 2, pp. 229-235, 1999.

[9] B. Kiumarsi, H. Modares, and F. Lewis, “Reinforcement learning for distributed control and multi-player games”, In Handbook of Reinforcement Learning and Control. Springer, Cham, pp. 7-27, 2021.

[10] K. G. Vamvoudakis, F. L. Lewis, and G. Hudas, “Multi-agent differential graphical games: Online adaptive learning solution for synchronization with optimality,” Automatica, vol. 48, no. 8, pp. 1598–1611, Aug. 2012.

[11] F. Tatari, M.B. Naghibi-Sistani, and K.G.  Vamvoudakis, “Distributed optimal synchronization control of linear networked systems under unknown dynamics”, American Control Conference(ACC), pp. 668-673, 2017.

[12] M. Abouheaf, F. L. Lewis, K. G. Vamvoudakis, Sofie Haesaert, and R. Babuska, “Multi-agent discrete-time graphical games and reinforcement learning solutions,” Automatica, vol. 50, no. 12, pp. 3038–3053, Dec. 2014.

[13] M. I. Abouheaf, F. L. Lewis, M. S. Mahmoud, and D. G. Mikulski, “Discrete-time dynamic graphical games: model-free reinforcement learning solution,” Control Theory and Technology, vol. 13, no. 1, pp. 55–69, Feb. 2015.

[14] S. Arora, & D. Prashant, “A survey of inverse reinforcement learning: Challenges, methods and progress”, Artificial Intelligence, vol. 297, 2021.

[15] X. Wang and D. Klabjan, “Competitive multi-agent inverse reinforcement learning with sub-optimal demonstrations,” In International Conference on Machine Learning, pp. 5143–5151, 2018.

[16] L. Yu, J. Song, and Stefano Ermon, “Multi-agent adversarial inverse reinforcement learning,” In International conference on machine learning , pp. 7194–7201, May 2019.

[17] C. Mu, K. Wang, Z. Ni, and C. Sun, “Cooperative differential game based optimal control and its application to power systems,” IEEE Trans. Ind. Informat., vol. 16, no. 8, pp. 5169–5179, Aug. 2020.

[18] B. Lian, W. Xue, F. L. Lewis, & T. Chai, “Inverse reinforcement learning for multi-player noncooperative apprentice games”, Automatica, vol. 145, pp. 110524, 2022.

[19] B. Lian, V. S. Donge, F. L. Lewis, T. Chai, and A. Davoudi, “Data-Driven Inverse Reinforcement Learning Control for Linear Multiplayer Games,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–14, 2022.

[20] V. S. Donge, B. Lian, F. L. Lewis, & A. Davoudi, “Multi-agent graphical games with inverse reinforcement learning”, IEEE Transactions on Control of Network Systems, vol. 10, no. 2, pp. 841 – 852, 2022.

[21] X. Li, Y. Tang, & H. R. Karimi, “Consensus of multi-agent systems via fully distributed event-triggered control”, Automatica, vol. 116, pp. 108898, 2020.

[22] X. Li, Z. Sun, Y. Tang, and Hamid Reza Karimi, “Adaptive event-triggered consensus of multiagent systems on directed graphs,” IEEE Transactions on Automatic Control, vol. 66, no. 4, pp. 1670–1685, Apr. 2021.

[23] S. Hu, D. Yue, X. Yin, X. Xie, and Y. Ma, “Adaptive event-triggered control for nonlinear discrete-time systems,” International Journal of Robust and Nonlinear Control, vol. 26, no. 18, pp. 4104–4125, Apr. 2016.

[24] L. Dong, X. Zhong, C. Sun, and H. He, “Adaptive event-triggered control based on heuristic dynamic programming for nonlinear discrete-time systems,” IEEE transactions on neural networks and learning systems , vol. 28, no. 7, pp. 1594–1605, Jul. 2017.

[25] W. Zhao, W. Yu, and H. Zhang, “Event-triggered optimal consensus tracking control for multi-agent systems with unknown internal states and disturbances,” Nonlinear Analysis: Hybrid Systems, vol. 33, pp. 227–248, Aug. 2019.

[26] S. Khoo, L. Xie, and Z. Man, “Robust finite-time consensus tracking

algorithm for multirobot systems,” IEEE Transactions on  Mechatronics, vol. 14, no. 2, pp. 219–228, 2009.

[27] H. Modares, F. L. Lewis and Z. Jiang, “  Tracking Control of Completely Unknown Continuous-Time Systems via Off-Policy   Reinforcement Learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2550-2562, Oct. 2015.

[28] Warren B. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality. vol. 703. Hoboken, NJ: John Wiley & Sons, Mar. 2007.