Practical Reinforcement Learning of Stabilizing Economic MPC

H. Bock and K. Plitt, “A multiple shooting algorithm for direct solution of optimal control problems,” in Proceedings 9th IFAC World Congress Budapest . Pergamon Press, 1984, pp. 242–247

1984

Earlier work this paper cites.

C. Watkins, “Learning from delayed rewards,” Ph.D. dissertation, King’s College, Cambridge, 1989

1989

Earlier work this paper cites.

F. Y. Wang and I. T. Cameron, “Control studies on a model evaporation process — constrained state driving with conventional and higher relative degree systems,” Journal of Process Control , vol. 4, pp. 59–75, 1994

1994

Earlier work this paper cites.

R. S. Sutton, D. McAllester, S. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Proceedings of the 12th International Conference on Neural Information Processing Systems , ser. NIPS’99. Cambridge, MA, USA: MIT Press, 1999, pp. 1057–1063

1999

Earlier work this paper cites.

P. Scokaert and J. Rawlings, “Feasibility Issues in Linear Model Predictive Control,” AIChE Journal , vol. 45, no. 8, pp. 1649–1659, 1999

1999

Earlier work this paper cites.

C. Büskens and H. Maurer, Online Optimization of Large Scale Systems . Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, ch. Sensitivity Analysis and Real-Time Optimization of Parametric Nonlinear Programming Problems, pp. 3–16

2001

Earlier work this paper cites.

C. Sonntag, O. Stursberg, and S. Engell, “Dynamic Optimization of an Industrial Evaporator using Graph Search with Embedded Nonlinear Programming,” in Proc. 2nd IFAC Conf. on Analysis and Design of Hybrid Systems (ADHS) , 2006, pp. 211–216

2006

Earlier work this paper cites.

P. Abbeel, A. Coates, M. Quigley, and A. Y. Ng, “An application of reinforcement learning to aerobatic helicopter flight,” in In Advances in Neural Information Processing Systems 19 . MIT Press, 2007, p. 2007

2007

Earlier work this paper cites.

J. Rawlings and D. Mayne, Model Predictive Control: Theory and Design . Nob Hill, 2009

2009

Earlier work this paper cites.

F. L. Lewis and D. Vrabie, “Reinforcement learning and adaptive dynamic programming for feedback control,” IEEE Circuits and Systems Magazine , vol. 9, no. 3, pp. 32–50, 2009

2009

Cited alongside, same era.

L. Grüne and J. Pannek, Nonlinear Model Predictive Control . London: Springer, 2011

2011

Cited alongside, same era.

S. Wang, W. Chaovalitwongse, and R. Babuska, “Machine learning algorithms in bipedal robot control,” IEEE Transactions on Systems, Man, and Cybernetics Part C , vol. 42, no. 5, pp. 728–743, Sep. 2012

2012

Cited alongside, same era.

F. L. Lewis, D. Vrabie, and K. G. Vamvoudakis, “Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers,” IEEE Control Systems , vol. 32, no. 6, pp. 76–105, 2012

2012

Cited alongside, same era.

G. Theocharous, P. S. Thomas, and M. Ghavamzadeh, “Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees,” in IJCAI , 2015, pp. 1806–1812

2015

Later among the works it cites.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of go with deep neural networks and tree search,” Nature , vol. 529, pp. 484–503, 2016

2016

Later among the works it cites.

C. J. Ostafew, A. P. Schoellig, and T. D. Barfoot, “Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking,” The International Journal of Robotics Research , vol. 35, no. 13, pp. 1547–1563, 2016

2016

Later among the works it cites.

M. Zanon, S. Gros, and M. Diehl, “A Tracking MPC Formulation that is Locally Equivalent to Economic MPC,” Journal of Process Control , 2016