Raza, Ali, Shah, Munam Ali, Khattak, Hasan Ali, Maple, Carsten, Al-Turjman, Fadi and Rauf, Hafiz Tayyab (2022) Collaborative multi-agents in dynamic industrial internet of things using deep reinforcement learning. Environment, Development and Sustainability, 24 . pp. 9481-9499. doi:10.1007/s10668-021-01836-9 ISSN 1387-585X.

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Abstract

Sustainable cities are envisioned to have economic and industrial steps toward reducing pollution. Many real-world applications such as autonomous vehicles, transportation, traffic signals, and industrial automation can now be trained using deep reinforcement learning (DRL) techniques. These applications are designed to take benefit of DRL in order to improve the monitoring as well as measurements in industrial internet of things for automation identification system. The complexity of these environments means that it is more appropriate to use multi-agent systems rather than a single-agent. However, in non-stationary environments multi-agent systems can suffer from increased number of observations, limiting the scalability of algorithms. This study proposes a model to tackle the problem of scalability in DRL algorithms in transportation domain. A partition-based approach is used in the proposed model to reduce the complexity of the environment. This partition-based approach helps agents to stay in their working area. This reduces the complexity of the learning environment and the number of observations for each agent. The proposed model uses generative adversarial imitation learning and behavior cloning, combined with a proximal policy optimization algorithm, for training multiple agents in a dynamic environment. We present a comparison of PPO, soft actor-critic, and our model in reward gathering. Our simulation results show that our model outperforms SAC and PPO in cumulative reward gathering and dramatically improved training multiple agents.

Item Type:	Journal Article
Subjects:	Q Science > Q Science (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Deep learning (Machine learning), Reinforcement learning, Multiagent systems, Internet of things
Journal or Publication Title:	Environment, Development and Sustainability
Publisher:	Springer Nature
ISSN:	1387-585X
Official Date:	July 2022
Dates:	Date Event July 2022 Published 15 March 2022 Available 15 September 2021 Accepted 10 June 2021 Submitted
Volume:	24
Number of Pages:	19
Page Range:	pp. 9481-9499
DOI:	10.1007/s10668-021-01836-9
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10668-021-01836-9
Access rights to Published version:	Restricted or Subscription Access
Copyright Holders:	The Author(s), under exclusive licence to Springer Nature B.V.
Date of first compliant deposit:	17 May 2022
Date of first compliant Open Access:	17 May 2022

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