A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system

•A model-based deep reinforcement learning (DRL) algorithm, which solves the Hamilton–Jacobi–Bellman equation for finite-horizon optimal control of nonlinear control-affine system is developed.•Deep neural networks (DNNs) are implemented to approximate the value function, its first-order derivative...

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Vydané v:	Journal of process control Ročník 87; s. 166 - 178
Hlavní autori:	Kim, Jong Woo, Park, Byung Jun, Yoo, Haeun, Oh, Tae Hoon, Lee, Jay H., Lee, Jong Min
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.03.2020
Predmet:	Approximate dynamic programming Deep neural networks Finite horizon optimal control problem Globalized dual heuristic programming Hamilton–Jacobi–Bellman equation Reinforcement learning Hamilton–Jacobi–Bellman equation Approximate dynamic programming Deep neural networks Finite horizon optimal control problem Globalized dual heuristic programming Reinforcement learning
ISSN:	0959-1524, 1873-2771
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