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Authors: Dhouha Ben Noureddine 1 ; Atef Gharbi 2 and Samir Ben Ahmed 3

Affiliations: 1 LISI, National Institute of Applied Science and Technology, INSAT, University of Carthage, FST and University of El Manar, Tunisia ; 2 LISI, National Institute of Applied Science and Technology, INSAT and University of Carthage, Tunisia ; 3 FST and University of El Manar, Tunisia

ISBN: 978-989-758-262-2

Keyword(s): Task Allocation, Multi-agent System, Deep Reinforcement Learning, Communication, Distributed Environment.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Multi-Agent Systems ; Software Engineering ; Symbolic Systems

Abstract: The task allocation problem in a distributed environment is one of the most challenging problems in a multiagent system. We propose a new task allocation process using deep reinforcement learning that allows cooperating agents to act automatically and learn how to communicate with other neighboring agents to allocate tasks and share resources. Through learning capabilities, agents will be able to reason conveniently, generate an appropriate policy and make a good decision. Our experiments show that it is possible to allocate tasks using deep Q-learning and more importantly show the performance of our distributed task allocation approach.

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Paper citation in several formats:
Ben Noureddine, D.; Gharbi, A. and Ben Ahmed, S. (2017). Multi-agent Deep Reinforcement Learning for Task Allocation in Dynamic Environment.In Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-262-2, pages 17-26. DOI: 10.5220/0006393400170026

@conference{icsoft17,
author={Dhouha Ben Noureddine. and Atef Gharbi. and Samir Ben Ahmed.},
title={Multi-agent Deep Reinforcement Learning for Task Allocation in Dynamic Environment},
booktitle={Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2017},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006393400170026},
isbn={978-989-758-262-2},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Multi-agent Deep Reinforcement Learning for Task Allocation in Dynamic Environment
SN - 978-989-758-262-2
AU - Ben Noureddine, D.
AU - Gharbi, A.
AU - Ben Ahmed, S.
PY - 2017
SP - 17
EP - 26
DO - 10.5220/0006393400170026

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