scholarly journals A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

2021 ◽  
Vol 1 (4) ◽  
pp. 257-270
Author(s):  
Ling Wang ◽  
Zixiao Pan ◽  
Jingjing Wang
Keyword(s):  
Reinforcement Learning ◽  
Manufacturing Scheduling ◽  
Intelligent Optimization

scholarly journals Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

2017 ◽  
Vol 7 (2) ◽  
pp. 136 ◽  
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas
Keyword(s):  
Reinforcement Learning ◽  
Petri Nets ◽  
Colored Petri Nets ◽  
Manufacturing Scheduling

scholarly journals An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhoubao Sun ◽  
Pengfei Chen ◽  
Xiaodong Zhang
Keyword(s):  
Reinforcement Learning ◽  
Superior Performance ◽  
Model Parameters ◽  
Optimization Strategy ◽  
Counting Problem ◽  
Intelligent Optimization ◽  
Decision Optimization ◽  
Step Counting ◽  
Filter Noise ◽  
Internet Of Things Technology

With the popularity of Internet of things technology and intelligent devices, the application prospect of accurate step counting has gained more and more attention. To solve the problems that the existing algorithms use threshold to filter noise, and the parameters cannot be updated in time, an intelligent optimization strategy based on deep reinforcement learning is proposed. In this study, the counting problem is transformed into a serialization decision optimization. This study integrates the noise recognition and the user feedback to update parameters. The end-to-end processing is direct, which alleviates the inaccuracy of step counting in the follow-up step counting module caused by the inaccuracy of noise filtering in the two-stage processing and makes the model parameters continuously updated. Finally, the experimental results show that the proposed model achieves superior performance to existing approaches.

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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