Safe Reinforcement Learning Using Robust MPC

Reinforcement learning (RL) has recently impressed the world with stunning results in various applications. While the potential of RL is now well established, many critical aspects still need to be tackled, including safety and stability issues. These issues, while secondary for the RL community, ar...

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Veröffentlicht in:	IEEE transactions on automatic control Jg. 66; H. 8; S. 3638 - 3652
Hauptverfasser:	Zanon, Mario, Gros, Sebastien
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Computational modeling Data models Learning Model accuracy Numerical models Optimization Predictive control Reinforcement learning (RL) robust model predictive control (MPC) Robustness Robustness (mathematics) safe policies Safety Stability analysis Uncertainty
ISSN:	0018-9286, 1558-2523
Online-Zugang:	Volltext
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