scholarly journals Output Resilient Containment Control of Heterogeneous Systems With Active Leaders Using Reinforcement Learning Under Attack Inputs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 162219-162228
Author(s):  
Qing Li ◽  
Lina Xia ◽  
Ruizhuo Song
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Systems ◽  
Containment Control

scholarly journals Containment Control of Heterogeneous Systems With Non-Autonomous Leaders: A Distributed Optimal Model Reference Approach

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 60689-60703 ◽  
Author(s):  
Yongliang Yang ◽  
Shusen Cheng ◽  
Yixin Yin ◽  
Donald C. Wunsch
Keyword(s):  
Heterogeneous Systems ◽  
Optimal Model ◽  
Containment Control ◽  
Model Reference

scholarly journals Optimal model-free output synchronization of heterogeneous systems using off-policy reinforcement learning

Automatica ◽  
2016 ◽  
Vol 71 ◽  
pp. 334-341 ◽  
Author(s):  
Hamidreza Modares ◽  
Subramanya P. Nageshrao ◽  
Gabriel A. Delgado Lopes ◽  
Robert Babuška ◽  
Frank L. Lewis
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Systems ◽  
Optimal Model ◽  
Model Free ◽  
Output Synchronization

scholarly journals Adaptive Load Balancing of Parallel Applications with Multi-Agent Reinforcement Learning on Heterogeneous Systems

2004 ◽  
Vol 12 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Johan Parent ◽  
Katja Verbeeck ◽  
Jan Lemeire ◽  
Ann Nowe ◽  
Kris Steenhaut ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Heterogeneous Systems ◽  
Machine Learning Techniques ◽  
Parallel Applications ◽  
Communication Overhead ◽  
Data Intensive ◽  
Multi Agent ◽  
State Coordination ◽  
Data Intensive Applications

We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.

Optimal Containment Control of Unknown Heterogeneous Systems With Active Leaders

2019 ◽  
Vol 27 (3) ◽  
pp. 1228-1236 ◽  
Author(s):  
Yongliang Yang ◽  
Hamidreza Modares ◽  
Donald C. Wunsch ◽  
Yixin Yin
Keyword(s):  
Heterogeneous Systems ◽  
Containment Control
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