Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning

James Duggan

Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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.

Original language	English (Ireland)
Title of host publication	AAMAS16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS MULTIAGENT SYSTEMS
Place of Publication	Singapore
Publication status	Published - 1 May 2016

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Mannion, P; Mason, K; Devlin, S; Duggan, J; Howley, E

Cite this

@inproceedings{02dccadff43f4245a191f1b819dec85f,

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.",

author = "James Duggan",

year = "2016",

month = may,

day = "1",

language = "English (Ireland)",

booktitle = "AAMAS16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS MULTIAGENT SYSTEMS",

}

TY - GEN

T1 - Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning

AU - Duggan, James

PY - 2016/5/1

Y1 - 2016/5/1

N2 - 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.

AB - 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.

M3 - Conference Publication

BT - AAMAS16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS MULTIAGENT SYSTEMS

CY - Singapore

ER -

Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

Fingerprint

Cite this