  We apply Reinforcement Learning algorithms to solve the classic quantitative
finance Market Making problem, in which an agent provides liquidity to the
market by placing buy and sell orders while maximizing a utility function. The
optimal agent has to find a delicate balance between the price risk of her
inventory and the profits obtained by capturing the bid-ask spread. We design
an environment with a reward function that determines an order relation between
policies equivalent to the original utility function. When comparing our agents
with the optimal solution and a benchmark symmetric agent, we find that the
Deep Q-Learning algorithm manages to recover the optimal agent.
