# Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market

**Authors:** Oscar Fernández Vicente, Fernando Fernández Rebollo, Francisco Javier García Polo  
**arXiv:** [2112.04494](https://arxiv.org/abs/2112.04494)  
**Date:** 2021-12-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2112.04494v1/

## Key points

- Study explores the role of market makers in financial markets
- Focus on providing liquidity through buy and sell limit orders
- Strategies employed by market makers from an agent-based perspective using Reinforcement Learning (RL)
- Investigation of RL market maker agents' behavior in non-competitive and competitive scenarios
- Examination of how agents adapt their strategies within a Sim2Real scope
- Exploration of policy transfer between different experiments and impact of competing environments on RL agent performance
- Utilization of RL and deep RL techniques demonstrates profitability for market makers
- Insights into intelligent market markers' behavior in stock markets and potential applications in real-world financial settings

## Summary

The study "Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market" by Oscar Fernández Vicente, Fernando Fernández Rebollo, and Francisco Javier García Polo explores the role of market makers in financial markets. The focus is on providing liquidity through buy and sell limit orders. The paper delves into the strategies employed by market makers from an agent-based perspective, specifically utilizing Reinforcement Learning (RL) to develop intelligent market markers in simulated stock markets. The research investigates how RL market maker agents behave in both non-competitive and competitive scenarios. It also examines how these agents adapt their strategies within a Sim2Real scope. Additionally, the study explores the application of policy transfer between different experiments and discusses the impact of competing environments on the performance of RL agents. Through the utilization of RL and deep RL techniques, the research demonstrates that these approaches are profitable for market makers, offering valuable insights into their behavior within stock markets. The findings contribute to a better understanding of how intelligent market markers can operate effectively in simulated environments and their potential applications in real-world financial settings.