Distributed reinforcement learning for the management of a smart grid interconnecting independent prosumers

In the context of an eco-responsible production and distribution of electrical energy at 2 the local scale of an urban territory, we consider a smart grid as a system interconnecting different 3 prosumers which all retain their decision-making autonomy and defend their own interests in a 4 comprehensive system which rules, accepted by all, encourage virtuous behavior. In this paper, 5 we present and analyse a model and a management method for smart grids, shared between 6 different kinds of independent actors, which respect their own interests and encourages each to a 7 behavior allowing as much as possible an energy independence of the smart grid from external 8 energy suppliers. We consider here a game theory model, in which each actor of the smart grid is 9 a player, and we investigate distributed machine learning algorithms to allow decision-making 10 leading the game to converge to stable situations, in particular Nash equilibrium. We propose a 11 Linear Reward Inaction algorithm that proves to achieve Nash equilibria most of the time, both for 12 a single time slot and across time, allowing the smart grid to maximize its energy independence 13 from external energy suppliers.