Chen, H. and Allgšwer, F. (1998). A quasi-infinite horizon
nonlinear model predictive control scheme with guaranteed
stability. Automatica, 34(10), 1205Ð1217.
Gros, S. and Zanon, M. (2019a). Towards safe reinforcement
learning using nmpc and policy gradients: Part
i-stochastic case. arXiv preprint arXiv:1906.04057.
Gros, S. and Zanon, M. (2019b). Towards safe reinforcement
learning using nmpc and policy gradients: Part
ii-deterministic case. arXiv preprint arXiv:1906.04034.
Gros, S., Zanon, M., and Bemporad, A. (2020). Safe
reinforcement learning via projection on a safe set: How
to achieve optimality? arXiv preprint arXiv:2004.00915.
Kim, J.W., Park, B.J., Yoo, H., Oh, T.H., Lee, J.H.,
and Lee, J.M. (2020). A model-based deep reinforcement
learning method applied to finite-horizon optimal
control of nonlinear control-affine system. Journal of
Process Control, 87, 166Ð178.
Lucia, S., Finkler, T., and Engell, S. (2013). Multi-stage
nonlinear model predictive control applied to a semibatch
polymerization reactor under uncertainty. Journal
of Process Control, 23(9), 1306Ð1319.
Mayne, D.Q., Kerrigan, E.C., Van Wyk, E., and Falugi, P.
(2011). Tube-based robust nonlinear model predictive
control. International Journal of Robust and Nonlinear
Control, 21(11), 1341Ð1353.
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A.,
Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013).
Playing atari with deep reinforcement learning. arXiv
preprint arXiv:1312.5602.
Schulman, J., Moritz, P., Levine, S., Jordan, M., and
Abbeel, P. (2015). High-dimensional continuous control
using generalized advantage estimation. arXiv preprint
arXiv:1506.02438.
Shin, J., Badgwell, T.A., Liu, K.H., and Lee, J.H. (2019).
Reinforcement learningÐoverview of recent progress and
implications for process control. Computers & Chemical
Engineering, 127, 282Ð294.
Spielberg, S.P.K., Gopaluni, R.B., and Loewen, P.D.
(2017). Deep reinforcement learning approaches for
process control. In 6th International Symposium on
Advanced Control of Industrial Processes (AdCONIP),
201Ð206.
Srinivasan, B., Bonvin, D., Visser, E., and Palanki, S.
(2003). Dynamic optimization of batch processes: Ii. role
of measurements in handling uncertainty. Computers &
chemical engineering, 27(1), 27Ð44.
Sutton, R.S., Barto, A.G., and Williams, R.J. (1992). Reinforcement
learning is direct adaptive optimal control.
IEEE Control Systems Magazine, 12(2), 19Ð22.
Sutton, R.S., McAllester, D.A., Singh, S.P., and Mansour,
Y. (2000). Policy gradient methods for reinforcement
learning with function approximation. In Advances in
neural information processing systems, 1057Ð1063.
Szepesv‡ri, C. (2010). Algorithms for reinforcement learning.
Synthesis lectures on artificial intelligence and
machine learning, 4(1), 1Ð103.
Wabersich, K.P. and Zeilinger, M.N. (2018). Safe exploration
of nonlinear dynamical systems: A predictive
safety filter for reinforcement learning. arXiv preprint
arXiv:1812.05506.
Zanon, M. and Gros, S. (2019). Safe reinforcement learning
using robust mpc. arXiv preprint arXiv:1906.04005.