Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning: (Extended Abstract)

Patrick Mannion, Karl Mason, Sam Devlin, Jim Duggan, Enda Howley

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


In this paper, we examine the application of Multi-Agent Reinforcement Learning (MARL) to a Dynamic Economic Emissions Dispatch problem. This is a multi-objective problem domain, where the conflicting objectives of fuel cost and emissions must be minimised. We evaluate the performance of several different MARL credit assignment structures in this domain, and our experimental results show that MARL can produce comparable solutions to those computed by Genetic Algorithms and Particle Swarm Optimisation.
Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Number of pages2
ISBN (Print)978-1-4503-4239-1
Publication statusPublished - 9 May 2016
EventInternational Conference on Autonomous Agents & Multiagent Systems -
Duration: 9 May 201613 May 2016


ConferenceInternational Conference on Autonomous Agents & Multiagent Systems
Abbreviated titleAAMAS

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