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Learning Pure Nash Equilibrium in Smart Charging Games
[article]
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
arXiv:2111.06817v1
fatcat:27i545n5cjg6flkqmnj7kftlxa