Language Support for Multi Agent Reinforcement Learning

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.