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Reinforcement learning (RL) and model predictive control (MPC) offer a wealth of distinct approaches for automatic decision-making under uncertainty.
F. Farshidian, D. Hoeller, M. Hutter, Deep Value Model Predictive Control, 2019. arXiv:1910.03358
1910
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1996
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Gain-scheduling trajectory control of a continuous stirred tank reactor,
K.-U. Klatt, S. Engell, · 1998
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Actor-critic algorithms,
V. Konda, J. Tsitsiklis, · 1999
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Constrained model predictive control: Stability and optimality,
D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. Scokaert, · 2000
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Neuro-dynamic programming method for MPC,
J. M. Lee, J. H. Lee, · 2001
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A survey of industrial model predictive control technology,
S. Qin, T. A. Badgwell, · 2003
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Robustness in Markov Decision Problems with Uncertain Transition Matrices,
A. Nilim, L. Ghaoui, · 2003
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Simulation-based learning of cost-to-go for control of nonlinear processes,
J. M. Lee, J. H. Lee, · 2004
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Identification for control: From the early achievements to the revival of experiment design,
M. Gevers, · 2005
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Robust model predictive control of constrained linear systems with bounded disturbances,
D. Q. Mayne, M. M. Seron, S. V. Raković, · 2005
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Stochastic Programming Applied to Model Predictive Control,
D. de la Penad, A. Bemporad, T. Alamo, · 2005
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Dynamic Programming and Suboptimal Control: A Survey from ADP to MPC*,
D. P. Bertsekas, · 2005
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2006
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Robust model predictive control: A survey,
A. Bemporad, M. Morari, · 2007
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Scenario-based model predictive control of stochastic constrained linear systems,
D. Bernardini, A. Bemporad, · 2009
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Model predictive control: Review of the three decades of development,
J. H. Lee, · 2011
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Blending MPC & Value Function Approximation for Efficient Reinforcement Learning,
M. Bhardwaj, S. Choudhury, B. Boots, · 2012
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2013
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Value function approximation and model predictive control,
M. Zhong, M. Johnson, Y. Tassa, T. Erez, E. Todorov, · 2013
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Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty,
S. Lucia, T. Finkler, S. Engell, · 2013
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An Easy Path to Convex Analysis and Applications,
B. S. Mordukhovich, N. M. Nam, · 2013
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Deterministic policy gradient algorithms,
D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, M. Riedmiller, · 2014
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Model predictive control in industry: Challenges and opportunities,
M. G. Forbes, R. S. Patwardhan, H. Hamadah, R. B. Gopaluni, · 2015
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Stochastic model predictive control: An overview and perspectives for future research,
A. Mesbah, · 2016
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F. Borrelli, A. Bemporad, M. Morari, Predictive Control for Linear and Hybrid Systems, Cambridge University Press, 2017
2017
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Stochastic model predictive control with active uncertainty learning: A Survey on dual control,
A. Mesbah, · 2017
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Hindsight Experience Replay,
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, W. Zaremba, · 2017
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Learning from the Hindsight Plan – Episodic MPC Improvement,
A. Tamar, G. Thomas, T. Zhang, S. Levine, P. Abbeel, · 2017
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2022
Later among the works it cites.
Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon?,
A. Mesbah, K. P. Wabersich, A. P. Schoellig, M. N. Zeilinger, S. Lucia, T. A. Badgwell, J. A. Paulson, · 2022
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Learning for MPC with stability & safety guarantees,
S. Gros, M. Zanon, · 2022
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Convex Neural Network-Based Cost Modifications for Learning Model Predictive Control,
K. Seel, A. B. Kordabad, S. Gros, J. T. Gravdahl, · 2022
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Neural Lyapunov Differentiable Predictive Control,
S. Mukherjee, J. Drgoňa, A. Tuor, M. Halappanavar, D. Vrabie, · 2022
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2017
Cited alongside, same era.
R. S. Sutton, A. G. Barto, Reinforcement Learning: An Introduction, Adaptive Computation and Machine Learning Series, second edition ed., The MIT Press, Cambridge, Massachusetts, 2018
2018
Cited alongside, same era.
Reinforcement learning for control: Performance, stability, and deep approximators,
L. Buşoniu, T. de Bruin, D. Tolić, J. Kober, I. Palunko, · 2018
Cited alongside, same era.
Nonlinear Model Predictive Control with Explicit Backoffs for Stochastic Systems under Arbitrary Uncertainty,
J. A. Paulson, A. Mesbah, · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,
T. Haarnoja, A. Zhou, P. Abbeel, S. Levine, · 2018
Cited alongside, same era.
The promise of artificial intelligence in chemical engineering: Is it here, finally?,
V. Venkatasubramanian, · 2019
Cited alongside, same era.
When to trust your model: Model-based policy optimization,
M. Janner, J. Fu, M. Zhang, S. Levine, · 2019
Cited alongside, same era.
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms,
S. Huang, R. F. J. Dossa, C. Ye, J. Braga, D. Chakraborty, K. Mehta, J. G. M. Araújo, · 2022
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Toward a Theoretical Foundation of Policy Optimization for Learning Control Policies,
B. Hu, K. Zhang, N. Li, M. Mesbahi, M. Fazel, T. Başar, · 2023
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Recurrent equilibrium networks: Flexible dynamic models with guaranteed stability and robustness,
M. Revay, R. Wang, I. R. Manchester, · 2023
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Do-mpc: Towards FAIR nonlinear and robust model predictive control,
F. Fiedler, B. Karg, L. Lüken, D. Brandner, M. Heinlein, F. Brabender, S. Lucia, · 2023
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Learning-based state estimation and control using MHE and MPC schemes with imperfect models,
H. Nejatbakhsh Esfahani, A. Bahari Kordabad, W. Cai, S. Gros, · 2023
Later among the works it cites.
T. Salzmann, E. Kaufmann, J. Arrizabalaga, M. Pavone, D. Scaramuzza, M. Ryll, · 2023
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Machine learning for industrial sensing and control: A survey and practical perspective,
N. P. Lawrence, S. K. Damarla, J. W. Kim, A. Tulsyan, F. Amjad, K. Wang, B. Chachuat, J. M. Lee, B. Huang, R. Bhushan Gopaluni, · 2024
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2024
Later among the works it cites.
Learning Lyapunov terminal costs from data for complexity reduction in nonlinear model predictive control,
S. Abdufattokhov, M. Zanon, A. Bemporad, · 2024
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2024
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The integration of Model Predictive Control and deep Reinforcement Learning for efficient thermal control in thermoforming processes,
H. Hosseinionari, R. Seethaler, · 2024
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TD-MPC2: Scalable, robust world models for continuous control,
N. Hansen, H. Su, X. Wang, · 2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior,
N. P. Lawrence, P. D. Loewen, S. Wang, M. G. Forbes, R. B. Gopaluni, · 2024
Later among the works it cites.
Learning for CasADi: Data-driven Models in Numerical Optimization,
T. Salzmann, J. Arrizabalaga, J. Andersson, M. Pavone, M. Ryll, · 2024
Later among the works it cites.
Local-Global Learning of Interpretable Control Policies: The Interface between MPC and Reinforcement Learning,
T. Banker, N. P. Lawrence, A. Mesbah, · 2025
Closest in time.
2025
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2025
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