  Stochastic optimal control usually requires an explicit dynamical model with
probability distributions, which are difficult to obtain in practice. In this
work, we consider the linear quadratic regulator (LQR) problem of unknown
linear systems and adopt a Wasserstein penalty to address the distribution
uncertainty of additive stochastic disturbances. By constructing an equivalent
deterministic game of the penalized LQR problem, we propose a Q-learning method
with convergence guarantees to learn an optimal minimax controller.
