We present a new financial framework where two families of RL-based agents representing the Liquidity Providers and Liquidity Takers learn simultaneously to satisfy their objective. Thanks to a parametrized reward formulation and the use of Deep RL, each group learns a shared policy able to generalize and interpolate over a wide range of behaviors. This is a step towards a fully RL-based market simulator replicating complex market conditions particularly suited to study the dynamics of the financial market under various scenarios.
@article{arxiv.2110.06829,
title = {Towards a fully RL-based Market Simulator},
author = {Leo Ardon and Nelson Vadori and Thomas Spooner and Mengda Xu and Jared Vann and Sumitra Ganesh},
journal= {arXiv preprint arXiv:2110.06829},
year = {2021}
}