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10.1007/s41403-020-00129-3 http://scihub22266oqcxt.onion/10.1007/s41403-020-00129-3 C7311597!7311597!38624387     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 276.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 276.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       ä-/-ä 2020 ; 5 (2): 129-32 Nephropedia Template TP {{tp|p=38624387|t=2020. Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning: An AI-Driven Control Approach Compatible with Existing Disease and Network Models |pdf=|usr=}}{{38624387}} gab.com Text Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning: An AI-Driven Control Approach Compatible with Existing Disease and Network Models JO: ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=38624387 #FOAvip There has been intense debate about lockdown policies in the context of Covid-19 for limiting damage both to health and to the economy. We present an AI-driven approach for generating optimal lockdown policies that control the spread of the disease while balancing both health and economic costs. Furthermore, the proposed reinforcement learning approach automatically learns those policies, as a function of disease and population parameters. The approach accounts for imperfect lockdowns, can be used to explore a range of policies using tunable parameters, and can be easily extended to fine-grained lockdown strictness. The control approach can be used with any compatible disease and network simulation models. Twit Text FOAVip Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning: An AI-Driven Control Approach Compatible with Existing Disease and Network Models ????ä-/-ä http://www.kidney.de/pget.php?&tw=1&pmid=38624387 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=38624387 #FOAvip English Wikipedia <ref>{{Cite pmid|38624387}}</ref> Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning: An AI-Driven Control Approach Compatible with Existing Disease and Network Models #MMPMID38624387 Khadilkar H; Ganu T; Seetharam DP ä-/-ä 2020[]; 5 (2): 129-32 PMID38624387show ga There has been intense debate about lockdown policies in the context of Covid-19 for limiting damage both to health and to the economy. We present an AI-driven approach for generating optimal lockdown policies that control the spread of the disease while balancing both health and economic costs. Furthermore, the proposed reinforcement learning approach automatically learns those policies, as a function of disease and population parameters. The approach accounts for imperfect lockdowns, can be used to explore a range of policies using tunable parameters, and can be easily extended to fine-grained lockdown strictness. The control approach can be used with any compatible disease and network simulation models. äOptimising Lockdown Policies for Epidemic Control using Reinforcement (epmc).pdf DeepDyve Pubget Overpricing