LEADER 02726nam 2200481 450 001 9910137529903321 005 20230621140740.0 010 $a9782889194315$b(ebook) 035 $a(CKB)3710000000569680 035 $a(WaSeSS)IndRDA00059078 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/61847 035 $a(EXLCZ)993710000000569680 100 $a20160713d2015 uy 0 101 0 $aeng 135 $aur||#|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aValue and reward based learning in neurorobots /$ftopic editors, Jeffrey L. Krichmar and Florian Ro?hrbein 210 $cFrontiers Media SA$d2015 210 1$a[Lausanne, Switzerland] :$cFrontiers Media SA,$d2015. 215 $a1 online resource (158 pages) $cillustrations; digital, PDF file(s) 225 0 $aFrontiers Research Topics 320 $aIncludes bibliographical references. 330 $aOrganisms are equipped with value systems that signal the salience of environmental cues to their nervous system, causing a change in the nervous system that results in modification of their behaviour. These systems are necessary for an organism to adapt its behaviour when an important environmental event occurs. A value system constitutes a basic assumption of what is good and bad for an agent. These value systems have been effectively used in robotic systems to shape behaviour. For example, many robots have used models of the dopaminergic system to reinforce behaviour that leads to rewards. Other modulatory systems that shape behaviour are acetylcholine?s effect on attention, norepinephrine?s effect on vigilance, and serotonin?s effect on impulsiveness, mood, and risk. Moreover, hormonal systems such as oxytocin and its effect on trust constitute as a value system. We seek to gather papers on research involving neurobiologically inspired robots whose behaviour is: 1) Shaped by value and reward learning, 2) adapted through interaction with the environment, and 3) shaped by extracting value from the environment. 606 $aReinforcement learning 610 $areward-based learning 610 $abasal ganglia 610 $aembodied cognition 610 $avalue system 610 $aneurorobotics 610 $aaction selection 610 $aNeuromodulation 610 $areinforcement learning 615 0$aReinforcement learning. 700 $aJeffrey L. Krichmar$4auth$01364630 702 $aKrichmar$b Jeffrey L. 702 $aRo?hrbein$b Florian 801 0$bWaSeSS 801 2$bUkMaJRU 912 $a9910137529903321 996 $aValue and reward based learning in neurorobots$93386069 997 $aUNINA