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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Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 484; S. 79 - 88
Hauptverfasser:	Mallick, Prakash, Chen, Zhiyiong, Zamani, Mohsen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.05.2022
Schlagworte:	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
Online-Zugang:	Volltext
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