# Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model

arXiv: https://arxiv.org/abs/2207.09951 · HTML: https://mlsanity.com/paper/2207.09951/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [Deep Learning modeling of Limit Order Book: a comparative perspective](https://mlsanity.com/paper/2007.07319/) (2020)
- [DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling](https://mlsanity.com/paper/1803.02998/) (2018)

## Built on by (most cited first, 2 of 2)

- [Deep Reinforcement Learning for Robust Goal-Based Wealth Management](https://mlsanity.com/paper/2307.13501/) (2023)
- [Reinforcement Learning with Hawkes-Structured Networks for Market Making](https://mlsanity.com/paper/2610.16524/) (2026)

## Abstract (arXiv)

The stochastic control problem of optimal market making is among the central problems in quantitative finance. In this paper, a deep reinforcement learning-based controller is trained on a weakly consistent, multivariate Hawkes process-based limit order book simulator to obtain market making controls. The proposed approach leverages the advantages of Monte Carlo backtesting and contributes to the line of research on market making under weakly consistent limit order book models. The ensuing deep reinforcement learning controller is compared to multiple market making benchmarks, with the results indicating its superior performance with respect to various risk-reward metrics, even under significant transaction costs.