论文标题
内在银行内借款和贷款平均现场控制游戏的强化学习
Reinforcement Learning for Intra-and-Inter-Bank Borrowing and Lending Mean Field Control Game
论文作者
论文摘要
我们为内部银行借贷和贷款问题提出了一个平均现场控制游戏模型。该框架允许研究合作银行群体之间出现的竞争游戏。该溶液是根据无限视野中两组之间的渐近nash平衡提供的。当模型未知时，使用三段时间的增强学习算法以数据驱动方式学习最佳借贷和贷款策略。经验数值分析表明，三个时间尺度的重要性，模型未知时的勘探策略的影响以及算法的收敛性。
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.
