?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Action+Selection+methods+using+Reinforcement+Learning&rft.creator=Humphrys%2C+Mark&rft.subject=Animal+Behavior&rft.subject=Ethology&rft.subject=Artificial+Intelligence&rft.subject=Dynamical+Systems&rft.subject=Machine+Learning&rft.subject=Robotics&rft.description=Action+Selection+schemes%2C+when+translated+into+precise+algorithms%2C+typically+involve+considerable+design+effort+and+tuning+of+parameters.+Little+work+has+been+done+on+solving+the+problem+using+learning.+This+paper+compares+eight+different+methods+of+solving+the+action+selection+problem+using+Reinforcement+Learning+(learning+from+rewards).+The+methods+range+from+centralised+and+cooperative+to+decentralised+and+selfish.+They+are+tested+in+an+artificial+world+and+their+performance%2C+memory+requirements+and+reactiveness+are+compared.+Finally%2C+the+possibility+of+more+exotic%2C+ecosystem-like+decentralised+models+are+considered.&rft.publisher=MIT+Press%2FBradford+Books&rft.contributor=Maes%2C+Pattie&rft.contributor=Mataric%2C+Maja&rft.contributor=Meyer%2C+Jean-Arcady&rft.contributor=Pollack%2C+Jordan&rft.contributor=Wilson%2C+Stewart+W.&rft.date=1996&rft.type=Conference+Paper&rft.type=NonPeerReviewed&rft.format=application%2Fpostscript&rft.identifier=http%3A%2F%2Fcogprints.org%2F447%2F2%2Fg.SAB96.ps&rft.identifier=++Humphrys%2C+Mark++(1996)+Action+Selection+methods+using+Reinforcement+Learning.++%5BConference+Paper%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F447%2F