Twice regularized MDPs and the equivalence between robustness and regularization

Robust Markov decision processes (MDPs) aim to handle changing or partially known system dynamics. To solve them, one typically resorts to robust optimization methods. However, this significantly increases computational complexity and limits scalability in both learning and planning. On the other ha...

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Bibliographische Detailangaben

Hauptverfasser:	Derman, Esther, Geist, Matthieu, Mannor, Shie
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Mathematics - Optimization and Control
Online-Zugang:	Volltext bestellen
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