Language Support for Multi Agent Reinforcement Learning

Tony Clark, Balbir Barn, Vinay Kulkarni, Souvik Barat

Research output: Chapter in Book/Report/Conference proceedingConference publication

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.

Original languageEnglish
Title of host publicationiSOFT - Proceedings of the 13th Innovations in Software Engineering Conference (Formerly known as India Software Engineering Conference), ISEC 2020
PublisherACM
ISBN (Electronic)9781450375948
DOIs
Publication statusPublished - 27 Feb 2020
Event13th Innovations in Software Engineering Conference, ISEC 2020 - Jabalpur, India
Duration: 27 Feb 202029 Feb 2020

Conference

Conference13th Innovations in Software Engineering Conference, ISEC 2020
CountryIndia
CityJabalpur
Period27/02/2029/02/20

Keywords

  • Agents
  • Reinforcement learning

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