Reinforcement learning using expectation maximization based guided policy search for stochastic dynamics
Guided policy search algorithms have been proven to work with incredible accuracy not only for controlling complicated dynamical systems, but also in learning optimal policies from exploration of various unseen instances. This paper deals with a trajectory optimization problem for an unknown dynamic...
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Published in | Neurocomputing (Amsterdam) Vol. 484; pp. 79 - 88 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.05.2022
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Subjects | |
Online Access | Get full text |
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