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http://hdl.handle.net/1942/989
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DC Field | Value | Language |
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dc.contributor.author | TUYLS, Karl | - |
dc.contributor.author | Croonenborghs, T. | - |
dc.contributor.author | Ramon, J. | - |
dc.contributor.author | Goetschalckx, R. | - |
dc.contributor.author | Bruynooghe, M. | - |
dc.date.accessioned | 2006-07-25T09:27:24Z | - |
dc.date.available | 2006-07-25T09:27:24Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS. p. 275-294 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/1942/989 | - |
dc.description.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. | - |
dc.format.extent | 347228 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | Springer | - |
dc.relation.ispartofseries | LECTURE NOTES IN COMPUTER SCIENCE | - |
dc.title | Multi-agent relational reinforcement learning | - |
dc.type | Journal Contribution | - |
local.bibliographicCitation.conferencename | ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS | - |
dc.identifier.epage | 294 | - |
dc.identifier.spage | 275 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.relation.ispartofseriesnr | 3394 | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.isi | 000228996700018 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.fullcitation | TUYLS, Karl; Croonenborghs, T.; Ramon, J.; Goetschalckx, R. & Bruynooghe, M. (2005) Multi-agent relational reinforcement learning. In: ADAPTIVE AGENTS AND MULTI-AGENT SYSTEMS. p. 275-294. | - |
item.contributor | TUYLS, Karl | - |
item.contributor | Croonenborghs, T. | - |
item.contributor | Ramon, J. | - |
item.contributor | Goetschalckx, R. | - |
item.contributor | Bruynooghe, M. | - |
crisitem.journal.issn | 0302-9743 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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multi_agent.pdf | Published version | 339.09 kB | Adobe PDF | View/Open |
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