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Revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems

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Proceedings of the International Conference on Aerospace System Science and Engineering 2019 (ICASSE 2019)

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

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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.

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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.

Author information

Authors and Affiliations

  1. Department of Aerospace Engineering, Ryerson University, Toronto, Canada

    Kaustav Jyoti Borah

  2. Department of Computer Science and Engineering, SMIT, Majitar, India

    Rajashree Talukdar

Authors
  1. Kaustav Jyoti Borah
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  2. Rajashree Talukdar
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Corresponding author

Correspondence to Kaustav Jyoti Borah .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Zhongliang Jing

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Borah, K.J., Talukdar, R. (2020). Revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems. In: Jing, Z. (eds) Proceedings of the International Conference on Aerospace System Science and Engineering 2019. ICASSE 2019. Lecture Notes in Electrical Engineering, vol 622. Springer, Singapore. https://doi.org/10.1007/978-981-15-1773-0_17

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