Policy iterations for reinforcement learning problems in continuous time and space -- fundamental theory and methods
DOI10.1016/j.automatica.2020.109421zbMath1461.93143arXiv1705.03520OpenAlexW3128350768MaRDI QIDQ2664203
Richard S. Sutton, Jae Young Lee
Publication date: 20 April 2021
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1705.03520
adaptive systemsreinforcement learningpolicy iterationiterative schemesoptimization under uncertaintiescontinuous time and space
Existence of optimal solutions belonging to restricted classes (Lipschitz controls, bang-bang controls, etc.) (49J30) Control/observation systems governed by ordinary differential equations (93C15) Iterative learning control (93B47)
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