LEADER 01767oam  2200481   450 
001 9910716739903321
005 20211229122121.0
035   $a(CKB)5470000002524778
035   $a(OCoLC)1264708444
035   $a(EXLCZ)995470000002524778
100   $a20210824j202104    ua             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aGrid-interactive multi-zone building control using reinforcement learning with global-local policy search $epreprint /$fXiangyu Zhang [and four others]
210  1$aGolden, CO :$cNational Renewable Energy Laboratory,$dApril 2021.
215   $a1 online resource (8 pages) $ccolor illustrations
225 1 $aNREL/CP ;$v2C00-78000
300   $a"April 2021."
320   $aIncludes bibliographical references (pages 7-8).
517   $aGrid-interactive multi-zone building control using reinforcement learning with global-local policy search 
606   $aElectric power consumption$zUnited States
606   $aEnergy consumption$zUnited States
606   $aElectric power consumption$2fast
606   $aEnergy consumption$2fast
607   $aUnited States$2fast
615  0$aElectric power consumption
615  0$aEnergy consumption
615  7$aElectric power consumption.
615  7$aEnergy consumption.
700   $aZhang$b Xiangyu$01400961
712 02$aNational Renewable Energy Laboratory (U.S.),
801  0$bGPO
801  1$bGPO
801  2$bOCLCO
801  2$bOCLCF
801  2$bGPO
906   $aBOOK
912   $a9910716739903321
996   $aGrid-interactive multi-zone building control using reinforcement learning with global-local policy search$93493607
997   $aUNINA