Learning Pure Nash Equilibrium in Smart Charging Games [article]

Benoît Sohet, Yezekael Hayel, Olivier Beaude, Alban Jeandin
2021 arXiv   pre-print
Reinforcement Learning Algorithms (RLA) are useful machine learning tools to understand how decision makers react to signals. It is known that RLA converge towards the pure Nash Equilibria (NE) of finite congestion games and more generally, finite potential games. For finite congestion games, only separable cost functions are considered. However, non-separable costs, which depend on the choices of all players instead of only those choosing the same resource, may be relevant in some
more » ... , like in smart charging games. In this paper, finite congestion games with non-separable costs are shown to have an ordinal potential function, leading to the existence of an action-dependent continuous potential function. The convergence of a synchronous RLA towards the pure NE is then extended to this more general class of congestion games. Finally, a smart charging game is designed for illustrating convergence of such learning algorithms.
arXiv:2111.06817v1 fatcat:27i545n5cjg6flkqmnj7kftlxa