Transfer of Reinforcement Learning Negotiation Policies: from Bilateral to Multilateral Scenarios

Ioannis Efstathiou, Oliver Lemon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Trading and negotiation dialogue capabilities have been identified as important in a variety of AI application areas. In prior work, it was shown how Reinforcement Learning (RL) agents in bilateral negotiations can learn to use manipulation in dialogue to deceive adversaries in non-cooperative trading games. In this paper we show that such trained policies can also be used effectively for multilateral negotiations, and can even outperform those which are trained in these multilateral environments. Ultimately, it is shown that training in simple bilateral environments (e.g. a generic version of "Catan") may suffice for complex multilateral non-cooperative trading scenarios (e.g. the full version of Catan).
Original languageEnglish
Title of host publicationECAI 2016 Proceedings
EditorsGal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen
PublisherIOS Press
Pages1640-1641
Number of pages2
Volume285
ISBN (Electronic)978-1-61499-672-9
ISBN (Print)978-1-61499-671-2
DOIs
Publication statusAccepted/In press - 8 Jun 2016
Event22nd European Conference on Artificial Intelligence - The Hague, Netherlands
Duration: 29 Aug 20162 Sep 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Volume285
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference22nd European Conference on Artificial Intelligence
Abbreviated titleECAI 2016
Country/TerritoryNetherlands
CityThe Hague
Period29/08/162/09/16

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