Refined Mean Field Optimization – REFINO
The main objective on this project is to provide an innovative framework for optimal control of stochastic distributed agents. Restless bandit allocation is one particular example where the control that can be sent to each arm is restricted to an on/off signal. The originality of this framework is the use of novel method, called refined mean field approximation, developed by the PI in the performance evaluation context. The use of this framework will allow the development of control heuristics that are asymptotically optimal as the number of arms goes to infinity and that also have a better performance than existing heuristics for a moderate number of arms. As an example, we will use this framework in the context of smart grids, to develop control policies for distributed electric appliances.
Monsieur Nicolas GAST (Centre de Recherche Inria Grenoble - Rhône-Alpes)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
INRIA GRA Centre de Recherche Inria Grenoble - Rhône-Alpes
Help of the ANR 252,288 euros
Beginning and duration of the scientific project: December 2019 - 48 Months