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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Vydané v:	Neurocomputing (Amsterdam) Ročník 484; s. 79 - 88
Hlavní autori:	Mallick, Prakash, Chen, Zhiyiong, Zamani, Mohsen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.05.2022
Predmet:	Expectation maximization Guided policy search Maximum likelihood Reinforcement learning Stochastic systems Trajectory optimization Expectation maximization Maximum likelihood Stochastic systems Guided policy search Trajectory optimization Reinforcement learning
ISSN:	0925-2312, 1872-8286
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