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Building collaboration in multi-agent systems using reinforcement learning

Aydin, Mehmet Emin; Fellows, Ryan


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Dr Mehmet Aydin
Senior Lecturer in Networks and Mobile Computing

Ryan Fellows


N.T. Nguyen

E. Pimenidis

Z. Khan

B. TrawiƄski


© Springer Nature Switzerland AG 2018. This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory, either. Particles are devised with Q learning for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced experimental results are supportive to the proposed idea suggesting that a substantive collaboration can be build via proposed learning algorithm.

Journal Article Type Conference Paper
Start Date Sep 5, 2018
Publication Date Jan 1, 2018
Journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 11056 LNAI
Pages 201-212
Book Title Computational Collective Intelligence
ISBN 9783319984452
APA6 Citation Aydin, M. E., & Fellows, R. (2018). Building collaboration in multi-agent systems using reinforcement learning. Lecture Notes in Artificial Intelligence, 11056 LNAI, 201-212.
Keywords agent collaboration, reinforcement learning, multi-agent systems, Q learning, disaster management
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Additional Information Additional Information : The final authenticated version is available online at
Title of Conference or Conference Proceedings : 10th International Conference, ICCCI 2018, Bristol, UK


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