LEADER 01190nam a2200289 i 4500 001 991001389929707536 005 20020507125240.0 008 000530s1995 ||| ||| | ita 020 $a8815050655 035 $ab10213934-39ule_inst 035 $aLE02985045$9ExL 040 $aISUFI - Sett. Diritti e Politiche Euromediterranee$bita 082 0 $a341.2422 110 2 $aCentre for economic policy research$0285032 245 13$aLa distribuzione dei poteri nell'Unione europea :$bun'analisi delle implicazioni del principio di sussidiarietà nel processo di integrazione europea /$cCentre for economic policy research 260 $aBologna :$bIl Mulino,$cc1995 300 $a198 p. ;$c22 cm 490 0 $aStudi e ricerche ;$v353. 500 $aTrad. di Giovanni Dognini 650 4$aSussidiarietà 740 0 $aMaking sense of subsidiarity 907 $a.b10213934$b17-02-17$c27-06-02 912 $a991001389929707536 945 $aLE029 341.2422 CEP01.01$g1$iLE029-1292$lle029$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i10262726$z27-06-02 996 $aDistribuzione dei poteri nell'Unione europea$9208124 997 $aUNISALENTO 998 $ale029$b01-01-00$cm$da $e-$feng$gxx $h3$i1 LEADER 03246nam 22005655 450 001 9910337637703321 005 20200703061129.0 010 $a3-030-10546-6 024 7 $a10.1007/978-3-030-10546-4 035 $a(CKB)4100000007522458 035 $a(MiAaPQ)EBC5639444 035 $a(DE-He213)978-3-030-10546-4 035 $a(PPN)233800468 035 $a(EXLCZ)994100000007522458 100 $a20190117d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Reinforcement Learning for Wireless Networks /$fby F. Richard Yu, Ying He 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (78 pages) 225 1 $aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112 311 $a3-030-10545-8 330 $aThis Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool. . 410 0$aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112 606 $aWireless communication systems 606 $aMobile communication systems 606 $aArtificial intelligence 606 $aElectrical engineering 606 $aWireless and Mobile Communication$3https://scigraph.springernature.com/ontologies/product-market-codes/T24100 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 615 0$aWireless communication systems. 615 0$aMobile communication systems. 615 0$aArtificial intelligence. 615 0$aElectrical engineering. 615 14$aWireless and Mobile Communication. 615 24$aArtificial Intelligence. 615 24$aCommunications Engineering, Networks. 676 $a006.31 676 $a006.31 700 $aYu$b F. Richard$4aut$4http://id.loc.gov/vocabulary/relators/aut$0864694 702 $aHe$b Ying$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337637703321 996 $aDeep Reinforcement Learning for Wireless Networks$91930060 997 $aUNINA