@inproceedings{981a13b237904cb6839ef39684844ada,
title = "Multi-objective dynamic dispatch optimisation using Multi-Agent Reinforcement Learning",
abstract = "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.",
keywords = "Difference rewards, Multi-Agent Systems, Multi-objective, Reinforcement learning, Reward shaping, Smart grid",
author = "Patrick Mannion and Karl Mason and Sam Devlin and Jim Duggan and Enda Howley",
note = "Publisher Copyright: Copyright {\textcopyright} 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 ; Conference date: 09-05-2016 Through 13-05-2016",
year = "2016",
language = "English",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "1345--1346",
booktitle = "AAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems",
}