Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241230 
Year of Publication: 
2021
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2021-7
Publisher: 
Bank of Canada, Ottawa
Abstract: 
This paper uses reinforcement learning (RL) to approximate the policy rules of banks participating in a high-value payments system. The objective of the agents is to learn a policy function for the choice of amount of liquidity provided to the system at the beginning of the day. Individual choices have complex strategic effects precluding a closed form solution of the optimal policy, except in simple cases. We show that in a simplified two-agent setting, agents using reinforcement learning do learn the optimal policy that minimizes the cost of processing their individual payments. We also show that in more complex settings, both agents learn to reduce their liquidity costs. Our results show the applicability of RL to estimate best-response functions in real-world strategic games.
Subjects: 
Payment clearing and settlement systems
Digital currencies and fintech
Financialsystem regulation and policies
Financial institutions
JEL: 
A12
C7
D83
E42
E58
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.