PropertyValue
?:abstract
  • This paper presents strategies for speeding up the convergence of agents on swarm. Speeding up the learning of an agent is a complex task since the choice of inadequate updating techniques may cause delays in the learning process or even induce an unexpected acceleration that causes the agent to converge to a non-satisfactory policy. We have developed strategies for updating policies which combines local and global search using past policies. Experimental results in dynamic environments of different dimensions have shown that the proposed strategies are able to speed up the convergence of the agents while achieving optimal action policies, improving the coordination of agents in the swarm while deliberating. ()
?:appearsInConferenceInstance
?:appearsInConferenceSeries
?:bookTitle
  • ICEIS ()
?:citationCount
  • 0 ()
?:cites
?:created
  • 2016-06-24 ()
?:creator
?:doi
  • 10.1007/978-3-319-09492-2_9 ()
?:endingPage
  • 153 ()
?:estimatedCitationCount
  • 0 ()
?:hasDiscipline
?:hasURL
?:language
  • en ()
?:publicationDate
  • 2013-07-04 ()
?:publisher
  • Springer, Cham ()
?:rank
  • 24188 ()
?:referenceCount
  • 27 ()
?:startingPage
  • 137 ()
?:title
  • Coordinating Agents in Dynamic Environment ()
?:type

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