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Revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems

Conference paper
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Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 622)

Abstract

Machine-learning techniques have been widely applied for solving decision-making problems. Machine-learning algorithms perform better as compared to other algorithms while dealing with complex environments. The recent development in the area of neural network has enabled reinforcement learning techniques to provide the optimal policies for sophisticated and capable agents. In this paper, we would like to explore some algorithms people have applied recently based on interaction of multiple agents and their components. We would like to provide a survey of reinforcement-learning techniques to solve complex and real-world scenarios.

Keywords

Machine learning Heterogeneous systems Multi Agents Q learning 

Notes

Acknowledgements

I would like to thank my wife Priyanka Talukdar, research scholar, department of Civil Engineering of IIT-Guwahati (India) for her valuable suggestions in shaping this paper. This survey was funded by Natural Sciences and Engineering Research Council (NSERC) Canada and my supervisor in Ryerson University, Canada.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Aerospace EngineeringRyerson UniversityTorontoCanada
  2. 2.Department of Computer Science and EngineeringSMITMajitarIndia

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