  Can deep reinforcement learning algorithms be exploited as solvers for
optimal trading strategies? The aim of this work is to test reinforcement
learning algorithms on conceptually simple, but mathematically non-trivial,
trading environments. The environments are chosen such that an optimal or
close-to-optimal trading strategy is known. We study the deep deterministic
policy gradient algorithm and show that such a reinforcement learning agent can
successfully recover the essential features of the optimal trading strategies
and achieve close-to-optimal rewards.
