  A corporate bond trader in a typical sell side institution such as a bank
provides liquidity to the market participants by buying/selling securities and
maintaining an inventory. Upon receiving a request for a buy/sell price quote
(RFQ), the trader provides a quote by adding a spread over a \textit{prevalent
market price}. For illiquid bonds, the market price is harder to observe, and
traders often resort to available benchmark bond prices (such as MarketAxess,
Bloomberg, etc.). In \cite{Bergault2023ModelingLI}, the concept of \textit{Fair
Transfer Price} for an illiquid corporate bond was introduced which is derived
from an infinite horizon stochastic optimal control problem (for maximizing the
trader's expected P\&L, regularized by the quadratic variation). In this paper,
we consider the same optimization objective, however, we approach the
estimation of an optimal bid-ask spread quoting strategy in a data driven
manner and show that it can be learned using Reinforcement Learning.
Furthermore, we perform extensive outcome analysis to examine the
reasonableness of the trained agent's behavior.
