REINFORCEMENT LEARNING METHODS FOR BANK CURRENCY RESERVE GAMES
In large-scale bank interaction systems,individual banks can adjust their borrowing and lending rates with the central bank to bring their currency reserves as close as possible to the sample mean,thereby reducing the probability of systemic risk.However,when the state process and parameters of the objective function are unknown,it is not directly possible to solve the stochastic differential game problem to obtain a Nash equilibrium.In this study,we combined mean-field game theory with relevant methods from continuous-time reinforcement learning to construct an approximate Nash equilibrium in a large-scale bank lending network.First,by solving the forward-backward coupled HJB-FPK equation,we obtained the mean-field equilibrium strategy representing the banks.Next,based on the form of the obtained strategy,we designed an iterative parameter method to characterize the approximate optimal strategy when parameters are unknown.Finally,using the learned parameters,we constructed an approximate Nash equilibrium for a large number of banks.
systemic riskReinforcement learningapproximate Nash equilibriumMean field games
万方数据
维普
