  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.
