[PDF][PDF] A mirror descent approach for mean field control applied to demande-side management
preprint, 2023•hal.science
We consider a finite-horizon Mean Field Control problem for Markovian models. The
objective function is composed of a sum of convex and Lipschitz functions taking their values
on a space of state-action distributions. We introduce an iterative algorithm which we prove
to be a Mirror Descent associated with a non-standard Bregman divergence, having a
convergence rate of order 1/
objective function is composed of a sum of convex and Lipschitz functions taking their values
on a space of state-action distributions. We introduce an iterative algorithm which we prove
to be a Mirror Descent associated with a non-standard Bregman divergence, having a
convergence rate of order 1/
Abstract
We consider a finite-horizon Mean Field Control problem for Markovian models. The objective function is composed of a sum of convex and Lipschitz functions taking their values on a space of state-action distributions. We introduce an iterative algorithm which we prove to be a Mirror Descent associated with a non-standard Bregman divergence, having a convergence rate of order 1/
hal.science
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