Language support for multi agent reinforcement learning

Clark, Tony, Barn, Balbir ORCID logoORCID: https://orcid.org/0000-0002-7251-5033, Kulkarni, Vinay and Barat, Souvik (2020) Language support for multi agent reinforcement learning. Proceedings of the 13th Innovations in Software Engineering Conference on Formerly Known as India Software Engineering Conference (ISEC 2020). In: 13th Innovations in Software Engineering Conference (ISEC), 27-29 Feb 2020, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India. ISBN 9781450375948. [Conference or Workshop Item] (doi:10.1145/3385032.3385041)

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Abstract

Software Engineering must increasingly address the issues of complexity and uncertainty that arise when systems are to be deployed into a dynamic software ecosystem. There is also interest in using digital twins of systems in order to design, adapt and control them when faced with such issues. The use of multi-agent systems in combination with reinforcement learning is an approach that will allow software to intelligently adapt to respond to changes in the environment. This paper proposes a language extension that encapsulates learning-based agents and system building operations and shows how it is implemented in ESL. The paper includes examples the key features and describes the application of agent-based learning implemented in ESL applied to a real-world supply chain.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 29093
Notes on copyright: © Clark, Tony and Barn, Balbir and Kulkarni, Vinay and Barat, Souvik | ACM 2020. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ISEC 2020: Proceedings of the 13th Innovations in Software Engineering Conference on Formerly known as India Software Engineering Conference, https://doi.org/10.1145/3385032.3385041
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Depositing User: Balbir Barn
Date Deposited: 18 Feb 2020 08:56
Last Modified: 09 Jun 2022 00:15
URI: https://eprints.mdx.ac.uk/id/eprint/29093

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