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Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017). Deep Reinforcement Learning: A Brief Survey. IEEE Signal Processing Magazine, 34, 26-38.
https://doi.org/10.1109/MSP.2017.2743240

has been cited by the following article:

• TITLE: Does Adaptive Learning Neutralize Interbank Market Liquidity Hoarding under a Distressed Market Condition?

  AUTHORS: Cheuk Yin Jeffrey Mo, Steve Y. Yang, Xingjia Zhang

  KEYWORDS: Reinforcement Learning, Agent-Based Modeling, Contagion Risk, Fire Sales, Network Topology, U.S.

  JOURNAL NAME: Theoretical Economics Letters, Vol.14 No.3, June 28, 2024

  ABSTRACT: This paper investigates how counterparty risk and precautionary liquidity concerns can lead to dry-up of liquidity in the interbank market with adaptive bank agents sharing common objectives of collecting as much interest payment while minimizing the insolvency risk. The agents are calibrated based on the US bank balance sheet data and are allowed to make autonomous decisions under different simulated market conditions. Through simulation studies, we observe an endogenously formed multi-layer lending network exhibiting the well-known core-periphery structure. Moreover, we find the adaptive learning banks would endogenously form liquidity hoarding phenomenon under an exogenous shock to a subset of the banks in the system. We also find that in an adaptive learning environment, fire sales would lead to a decrease in interbank liquidity and increase the probability of contagion through interbank lending networks.