Abstract
In this chapter, it is seen that distributed control protocols that both guarantee synchronization and are globally optimal for the multi-agent team always exist on any sufficiently connected communication graph if a different definition of optimality is used. To this end, we study the notion of Nash equilibrium for multiplayer games on graphs. This leads us to the idea of a new sort of differential game—graphical games. In graphical games, each agent has its own dynamics as well as its own local performance index. The dynamics and local performance indices of each agent are distributed; they depend on the state of the agent, the control of the agent, and the controls of the agent’s neighbors. We show how to compute distributed control protocols that guarantee global Nash equilibrium for multi-agent teams on any graph that has a spanning tree.
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References
Abou-Kandil H, Freiling G, Ionescu V, Jank G (2003) Matrix Riccati Equations in Control and Systems Theory. Birkhäuser
Abu-Khalaf M, Lewis FL (2005) Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach. Automatica 41(5):779–791
Al-Tamimi A, Abu-Khalaf M, Lewis FL (2007) Adaptive critic designs for discrete-time zero-sum games with application to H-Infinity control. IEEE Trans Syst, Man, Cybern B 37(1):240–247
Al-Tamimi A, Lewis FL, Abu-Khalaf M (2008) Discrete-time nonlinear HJB solution using approximate dynamic programming: convergence proof. IEEE Trans Syst, Man, Cybern B 38(4):943–949
Başar T, Olsder GJ (1999) Dynamic Noncooperative Game Theory, 2nd edn. SIAM, Philadelphia
Bertsekas DP, Tsitsiklis JN (1996) Neuro-Dynamic Programming. Athena Scientific, Belmont
Brewer JW (1978) Kronecker products and matrix calculus in system theory. IEEE Trans Circuits Syst 25:772–781
Busoniu L, Babuska R, De Schutter B (2008) A comprehensive survey of multi-agent reinforcement learning. IEEE Trans Syst, Man, Cybern C 38(2):156–172
Dierks T, Jagannathan S (2010) Optimal control of affine nonlinear continuous-time systems using an online Hamilton–Jacobi–Isaacs formulation. In: Proc. IEEE Conf. Decision Control, Atlanta, GA, pp. 3048–3053
Freiling G, Jank G, Abou-Kandil H (2002) On global existence of solutions to coupled matrix Riccati equations in closed loop Nash games. IEEE Trans Automat Contr 41(2):264–269
Gajic Z, Li T-Y (1988) Simulation results for two new algorithms for solving coupled algebraic Riccati equations. Paper presented at 3rd international symposium on differential games, Sophia Antipolis, Nice, France
Goldberg AV (1995) Scaling algorithms for the shortest paths problem. SIAM J Comput 24:494–504
Ioannou P, Fidan B (2006) Adaptive Control Tutorial. SIAM, Philadelphia
Johnson M, Hiramatsu T, Fitz-Coy N, Dixon WE (2010) Asymptotic stackelberg optimal control design for an uncertain euler lagrange system. In: Proc. IEEE Conf. Decision Control, Atlanta, GA, pp. 6686–6691
Kakade S, Kearns M, Langford J, Ortiz L (2003) Correlated equilibria in graphical games. In: the 4th ACM conf. Electron. Commerce, San Diego, CA, pp. 42–47
Kearns M, Littman M, Singh S (2001) Graphical models for game theory. In: Proc. Annual conf. Uncertainty in Artificial Intelligence, Seattle, WA, pp. 253–260
Khoo S, Xie L, Man Z (2009) Robust finite-time consensus tracking algorithm for multirobot systems. IEEE Trans Mechatron 14:219–228
Leake RJ, Liu R-W (1967) Construction of suboptimal control sequences. SIAM J Contr 5(1):54–63
Lewis FL (1992) Applied Optimal Control and Estimation: Digital Design and Implementation. Prentice-Hall, Upper Saddle River
Lewis FL, Vrabie D (2009) Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits & Systems Magazine (invited feature article), pp. 32–50, Third Quarter 2009
Lewis FL, Jagannathan S, Yesildirek A (1999) Neural Network Control of Robot Manipulators and Nonlinear Systems. Taylor and Francis, London
Lewis FL, Vrabie D, Syrmos VL (2012) Optimal control, 3rd edn. Wiley, Hoboken
Lewis FL, Vrabie D, Vamvoudakis KG (2012) Reinforcement learning and feedback control. IEEE Control Systems Magazine, pp. 76–105
Li X, Wang X, Chen G (2004) Pinning a complex dynamical network to its equilibrium. IEEE Trans Circuits Syst I, Reg Papers 51(10):2074–2087
Littman ML (2001) Value-function reinforcement learning in Markov games. J Cogn Syst Res 2(1):55–66
Marden JR, Young HP, Pao LY (2012) Achieving pareto optimality through distributed learning. In: Proc. IEEE Conf. Decision Control, Maui, HI, pp. 7419–7424
Shinohara R (2010) Coalition proof equilibria in a voluntary participation game. Int J Game Theory 39(4):603–615
Shoham Y, Leyton-Brown K (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, Cambridge
Sutton RS, Barto AG (1998) Reinforcement learning—an introduction. MIT Press, Cambridge
Tijs S (2003) Introduction to game theory. Hindustan Book Agency, New Delhi.
Vamvoudakis KG, Lewis FL (2010). Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem. Automatica 46(5):878–888
Vamvoudakis KG, Lewis FL (2011). Multi-player non-zero sum games: online adaptive learning solution of coupled Hamilton–Jacobi equations. Automatica 47(8):1556–1569
Vamvoudakis KG, Lewis FL, Hudas GR (2012) Multi-agent differential graphical games: online adaptive learning solution for synchronization with optimality. Automatica 48(8):1598–1611
Vrabie D, Lewis FL (2009) Neural network approach to continuous-time direct adaptive optimal control for partially-unknown nonlinear systems. Neural Networks 2(3):237–246
Vrabie D, Pastravanu O, Lewis FL, Abu-Khalaf M (2009). Adaptive optimal control for continuous-time linear systems based on policy iteration. Automatica 45(2):477–484
Vrancx P, Verbeeck K, Nowe A (2008). Decentralized learning in Markov games. IEEE Tran Syst Man Cyber 38(4):976–981
Wang F, Zhang H, Liu D (May 2009) Adaptive dynamic programming: an introduction. IEEE Comput Intell Mag 4(2):39–47
Wang X, Chen G (2002). Pinning control of scale-free dynamical networks. Physica A 310(3–4):521–531
Werbos PJ (1974) Beyond Regression: New Tools for Prediction and Analysis in the Behavior Sciences. Ph.D. Thesis, Harvard University
Werbos PJ (1992) Approximate dynamic programming for real-time control and neural modeling. In: White DA, Sofge DA (eds) Handbook of Intelligent Control. Van Nostrand Reinhold, New York
Zwick U (2002) All pairs shortest paths using bridging sets and rectangular matrix multiplication. J ACM 49(3):289-317.
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Lewis, F., Zhang, H., Hengster-Movric, K., Das, A. (2014). Graphical Games: Distributed Multiplayer Games on Graphs. In: Cooperative Control of Multi-Agent Systems. Communications and Control Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-5574-4_6
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DOI: https://doi.org/10.1007/978-1-4471-5574-4_6
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