  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.
