  We study the problem of learning a Nash equilibrium (NE) in an imperfect
information game (IIG) through self-play. Precisely, we focus on two-player,
zero-sum, episodic, tabular IIG under the perfect-recall assumption where the
only feedback is realizations of the game (bandit feedback). In particular, the
dynamic of the IIG is not known -- we can only access it by sampling or
interacting with a game simulator. For this learning setting, we provide the
Implicit Exploration Online Mirror Descent (IXOMD) algorithm. It is a
model-free algorithm with a high-probability bound on the convergence rate to
the NE of order $1/\sqrt{T}$ where $T$ is the number of played games. Moreover,
IXOMD is computationally efficient as it needs to perform the updates only
along the sampled trajectory.
