Segmented Actor-Critic-Advantage Architecture for Reinforcement Learning Tasks

被引：0

作者：

Kaloev, Martin ^{[1
]}

Krastev, Georgi ^{[1
]}

机构：

[1] Univ Ruse, Dept Comp Syst & Technol, Ruse, Bulgaria

来源：

TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS | 2022年 / 11卷 / 01期

关键词：

Reinforcement learning; Q-learning; Actor-critic algorithm; Neuron-like machine architecture;

D O I：

10.18421/TEM111-27

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The article focuses on experiments with a multi module neural networks type of architecture for neuron-like machine used in reinforcing learning. This type of architecture can be used to solve complex robotic or policy optimization tasks and allows segmented storage of trained memory. Such technique speeds up the training process compared to existing actor-critical algorithms.

引用

页码：219 / 224

页数：6

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