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

被引：0

作者：

Mannion, Patrick ^{[1
]}

Mason, Karl ^{[1
]}

Devlin, Sam ^{[2
]}

Duggan, Jim ^{[1
]}

Howley, Enda ^{[1
]}

机构：

[1] Natl Univ Ireland, Galway, Ireland

[2] Univ York, York, N Yorkshire, England

来源：

AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS | 2016年

基金：

英国工程与自然科学研究理事会;

关键词：

Multi-objective; Reinforcement Learning; Reward Shaping; Difference Rewards; Multi-Agent Systems; Smart Grid;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1345 / 1346

页数：2

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