# Reinforcement Learning for Intra-and-Inter-Bank Borrowing and Lending Mean Field Control Game

arXiv: https://arxiv.org/abs/2207.03449 · HTML: https://mlsanity.com/paper/2207.03449/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Deep Reinforcement Learning for Infinite Horizon Mean Field Problems in Continuous Spaces](https://mlsanity.com/paper/2309.10953/) (2023)
- [Analysis of Multiscale Reinforcement Q-Learning Algorithms for Mean Field Control Games](https://mlsanity.com/paper/2405.17017/) (2024)
- [Convergence of Actor-Critic Learning for Mean Field Games and Mean Field Control in Continuous Spaces](https://mlsanity.com/paper/2511.06812/) (2025)

## Abstract (arXiv)

We propose a mean field control game model for the intra-and-inter-bank borrowing and lending problem. This framework allows to study the competitive game arising between groups of collaborative banks. The solution is provided in terms of an asymptotic Nash equilibrium between the groups in the infinite horizon. A three-timescale reinforcement learning algorithm is applied to learn the optimal borrowing and lending strategy in a data driven way when the model is unknown. An empirical numerical analysis shows the importance of the three-timescale, the impact of the exploration strategy when the model is unknown, and the convergence of the algorithm.