Policy Search Reinforcement Learning Method in Latent Space

Policy Search Reinforcement Learning Method in Latent Space

Policy search is an efficient learning method in the field of deep reinforcement learning (DRL), which is capable of solving large-scale problems with continuous state and action spaces and widely used in real-world problems. However, such method usually requires a large number of trajectory samples...

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Bibliographic Details
Main Author: ZHAO Tingting, WANG Ying, SUN Wei, CHEN Yarui, WANG Yuan, YANG Jucheng
Format: Article
Language:zho
Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2024-04-01
Series:Jisuanji kexue yu tansuo
Subjects:
model-free reinforcement learning; policy model; state representations; action representations; continuous action space; policy search reinforcement learning method
Online Access:http://fcst.ceaj.org/fileup/1673-9418/PDF/2211106.pdf

Internet

http://fcst.ceaj.org/fileup/1673-9418/PDF/2211106.pdf

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