LEADER 01605nam0 22003733i 450 001 VAN00276140 005 20240806101544.395 017 70$2N$a9783658391799 100 $a20240521d2022 |0itac50 ba 101 $aeng 102 $aDE 105 $a|||| ||||| 200 1 $aGeneric Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling$fSchirin Bär 210 $aWiesbaden$cSpringer Vieweg$d2022 215 $axxii, 148 p.$cill.$d24 cm 606 $a68-XX$xComputer science [MSC 2020]$3VANC019670$2MF 606 $a97-XX$xMathematics education [MSC 2020]$3VANC023813$2MF 610 $aFlexible Manufacturing$9KW:K 610 $aJob Shop Scheduling$9KW:K 610 $aMachine learning$9KW:K 610 $aMulti-Agent System$9KW:K 610 $aProduction Scheduling$9KW:K 610 $aReinforcement Learning$9KW:K 620 $aDE$dWiesbaden$3VANL000457 700 1$aBär$bSchirin$3VANV228837$01261730 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20241115$gRICA 856 4 $uhttps://doi.org/10.1007/978-3-658-39179-9$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN00276140 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08DLOAD e-Book 8568 $e08eMF8568 20240604 996 $aGeneric Multi-Agent Reinforcement Learning Approach for Flexible Job-Shop Scheduling$92940556 997 $aUNICAMPANIA