scholarly journals Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems

10.5772/5279 ◽  
2008 ◽  
Author(s):  
Jim Dowling ◽  
Seif Haridi
Keyword(s):  
Reinforcement Learning ◽  
Distributed Systems ◽  
Online Optimization

Reinforcement Learning's Contribution to the Cyber Security of Distributed Systems

2020 ◽  
Vol 12 (2) ◽  
pp. 35-55
Author(s):  
Christophe Feltus
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Information System ◽  
Reinforcement Learning ◽  
Distributed Systems ◽  
Unsupervised Learning ◽  
Cyber Security ◽  
Analytic Framework ◽  
Learning Paradigm

Reinforcement learning (RL) is a machine learning paradigm, like supervised or unsupervised learning, which learns the best actions an agent needs to perform to maximize its rewards in a particular environment. Research into RL has been proven to have made a real contribution to the protection of cyberphysical distributed systems. In this paper, the authors propose an analytic framework constituted of five security fields and eight industrial areas. This framework allows structuring a systematic review of the research in artificial intelligence that contributes to cybersecurity. In this contribution, the framework is used to analyse the trends and future fields of interest for the RL-based research in information system security.

Reinforcement Learning for Online Optimization of Banner Format and Delivery

2011 ◽  
pp. 13-31
Author(s):  
Benoit Baccot ◽  
Romulus Grigoras ◽  
Vincent Charvillat
Keyword(s):  
Reinforcement Learning ◽  
Ground Truth ◽  
Test Site ◽  
Online Optimization ◽  
Optimal Advertising

Results, showing the power and the efficiency of the two models to solve our problems, are also given. By comparing to a “ground truth” acquired by observing user browsing session on a test site, we conclude that our models are able to determine optimal advertising policies concerning banner formats and delivery.

An Artificial Intelligence Approach for Online Optimization of Flexible Manufacturing Systems

2018 ◽  
Vol 882 ◽  
pp. 96-108 ◽  
Author(s):  
Jupiter Bakakeu ◽  
Schirin Tolksdorf ◽  
Jochen Bauer ◽  
Hans-Henning Klos ◽  
Jörn Peschke ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Learning Algorithm ◽  
Electricity Consumption ◽  
Control Policy ◽  
Online Optimization ◽  
Energy Prices ◽  
Sequential Decision ◽  
Time Step

This paper addresses the problem of efficiently operating a flexible manufacturing machine in an electricity micro-grid featuring a high volatility of electricity prices. The problem of finding the optimal control policy is formulated as a sequential decision making problem under uncertainty where, at every time step the uncertainty comes from the lack of knowledge about fu-ture electricity consumption and future weather dependent energy prices. We propose to address this problem using deep reinforcement learning. To this purpose, we designed a deep learning architecture to forecast the load profile of future manufacturing schedule from past production time series. Combined with the forecast of future energy prices, the reinforcement-learning algorithm is trained to perform an online optimization of the production ma-chine in order to reduce the long-term energy costs. The concept is empirical-ly validated on a flexible production machine, where the machine speed can be optimized during the production.

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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