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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 | Source: | ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS. p. 275-294 | Series/Report: | LECTURE NOTES IN COMPUTER SCIENCE | Series/Report no.: | 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. | Document URI: | http://hdl.handle.net/1942/989 | ISSN: | 0302-9743 | ISI #: | 000228996700018 | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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multi_agent.pdf | Published version | 339.09 kB | Adobe PDF | View/Open |
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