Estimating policy functions in payments systems using reinforcement learning / by Pablo S. Castro, Ajit Desai, Han Du, Rodney Garratt and Francisco Rivadeneyra.: FB3-5/2021-7E-PDF
"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"--Abstract, page ii.
Permanent link to this Catalogue record:
publications.gc.ca/pub?id=9.895997&sl=0
| Department/Agency | Bank of Canada, issuing body. |
|---|---|
| Title | Estimating policy functions in payments systems using reinforcement learning / by Pablo S. Castro, Ajit Desai, Han Du, Rodney Garratt and Francisco Rivadeneyra. |
| Series title | Staff working paper = Document de travail du personnel, 1701-9397 ; 2021-7 |
| Publication type | Series - View Master Record |
| Language | [English] |
| Format | Electronic |
| Electronic document | |
| Note(s) | Includes bibliographical references (pages 20-21). |
| Publishing information | Ottawa, Ontario, Canada : Bank of Canada = Banque du Canada, February 1, 2021. ©2021 |
| Author / Contributor | Castro, Pablo S., author. |
| Description | 1 online resource (ii, 40 pages) : colour graphs |
| Catalogue number |
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| Subject terms | Banks and banking. Reinforcement learning. Payment. Banques. Apprentissage par renforcement (Intelligence artificielle) Paiement. |
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