Bidding in Non-Stationary Energy Markets

被引：0

作者：

Hernandez-Leal, Pablo ^{[1
]}

Taylor, Matthew E. ^{[2
]}

Munoz de Cote, Enrique ^{[1
]}

Enrique Sucar, L. ^{[1
]}

机构：

[1] Inst Nacl Astrofis Opt & Electr, Puebla, Mexico

[2] Washington State Univ, Pullman, WA 99164 USA

来源：

PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (AAMAS'15) | 2015年

关键词：

PowerTAC; opponent modeling; non-stationary strategies; Markov decision processes; energy markets;

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：

The PowerTAC competition has gained attention for being a realistic and powerful simulation platform used for research on retail energy markets, in part because of the growing number of energy markets worldwide. Agents in this complex environment typically use multiple strategies, changing from one to another, posing a problem for current learning algorithms. This paper introduces DriftER, an algorithm that learns an opponent model and tracks its error rate. We compare our algorithm in the PowerTAC simulator against the champion of the 2013 competition and a state of the art algorithm tailored for interacting against switching (non-stationary) opponents. The results show that DriftER outperforms the competition in terms of profit and accuracy.

引用

页码：1709 / 1710

页数：2

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