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

Enda Howley

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

Enda Howley

Computer Science

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	Multi-Objective Dynamic Dispatch Optimisation using Multi-Agent Reinforcement Learning
Number of pages	2
Publication status	Published - 1 Jan 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

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

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.",

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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.

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

Abstract

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

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Cite this