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10.1140/epjp/s13360-021-01620-8 http://scihub22266oqcxt.onion/10.1140/epjp/s13360-021-01620-8 34094796!8166378!34094796     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+Phys+J+Plus 2021 ; 136 (5): 609 Nephropedia Template TP {{tp|p=34094796|t=2021. Application of reinforcement learning for effective vaccination strategies of coronavirus disease 2019 (COVID-19) |pdf=|usr=}}{{34094796}} gab.com Text Application of reinforcement learning for effective vaccination strategies of coronavirus disease 2019 (COVID-19) JO: Eur Phys J Plus http://www.kidney.de/pget.php?&tw=1&pmid=34094796 #FOAvip Since December 2019, the new coronavirus has raged in China and subsequently all over the world. From the first days, researchers have tried to discover vaccines to combat the epidemic. Several vaccines are now available as a result of the contributions of those researchers. As a matter of fact, the available vaccines should be used in effective and efficient manners to put the pandemic to an end. Hence, a major problem now is how to efficiently distribute these available vaccines among various components of the population. Using mathematical modeling and reinforcement learning control approaches, the present article aims to address this issue. To this end, a deterministic Susceptible-Exposed-Infectious-Recovered-type model with additional vaccine components is proposed. The proposed mathematical model can be used to simulate the consequences of vaccination policies. Then, the suppression of the outbreak is taken to account. The main objective is to reduce the effects of Covid-19 and its domino effects which stem from its spreading and progression. Therefore, to reach optimal policies, reinforcement learning optimal control is implemented, and four different optimal strategies are extracted. Demonstrating the efficacy of the proposed methods, finally, numerical simulations are presented. Twit Text FOAVip Application of reinforcement learning for effective vaccination strategies of coronavirus disease 2019 (COVID-19) ????Eur Phys J Plus http://www.kidney.de/pget.php?&tw=1&pmid=34094796 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34094796 #FOAvip English Wikipedia <ref>{{Cite pmid|34094796}}</ref> Application of reinforcement learning for effective vaccination strategies of coronavirus disease 2019 (COVID-19) #MMPMID34094796 Beigi A; Yousefpour A; Yasami A; Gomez-Aguilar JF; Bekiros S; Jahanshahi H Eur Phys J Plus 2021[]; 136 (5): 609 PMID34094796show ga Since December 2019, the new coronavirus has raged in China and subsequently all over the world. From the first days, researchers have tried to discover vaccines to combat the epidemic. Several vaccines are now available as a result of the contributions of those researchers. As a matter of fact, the available vaccines should be used in effective and efficient manners to put the pandemic to an end. Hence, a major problem now is how to efficiently distribute these available vaccines among various components of the population. Using mathematical modeling and reinforcement learning control approaches, the present article aims to address this issue. To this end, a deterministic Susceptible-Exposed-Infectious-Recovered-type model with additional vaccine components is proposed. The proposed mathematical model can be used to simulate the consequences of vaccination policies. Then, the suppression of the outbreak is taken to account. The main objective is to reduce the effects of Covid-19 and its domino effects which stem from its spreading and progression. Therefore, to reach optimal policies, reinforcement learning optimal control is implemented, and four different optimal strategies are extracted. Demonstrating the efficacy of the proposed methods, finally, numerical simulations are presented. äApplication of reinforcement learning for effective vaccination strate(epmc).pdf DeepDyve Pubget Overpricing