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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/989

Title: Multi-agent relational reinforcement learning
Authors: TUYLS, Karl
Croonenborghs, T.
Ramon, J.
Goetschalckx, R.
Bruynooghe, M.
Issue Date: 2005
Publisher: Springer
Citation: ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS. p. 275-294
Series/Report: LECTURE NOTES IN COMPUTER SCIENCE, 3394
Abstract: In this paper we study Relational Reinforcement Learning in a multi-agent setting. There is growing evidence in the Reinforcement Learning research community that a relational representation of the state space has many bene ts over a propositional one. Complex tasks as planning or information retrieval on the web can be represented more naturally in relational form. Yet, this relational structure has not been exploited for multi-agent reinforcement learning tasks and has only been studied in a single agent context so far. This paper is a rst attempt in bridging the gap between Relation Reinforcement Learning (RRL) and Multi-agent Systems (MAS). More precisely, we will explore how a relational structure of the state space can be used in a Multi-Agent Reinforcement Learning context.
URI: http://hdl.handle.net/1942/989
ISI #: 000228996700018
ISSN: 0302-9743
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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