  The current state of the art in playing many important perfect information
games, including Chess and Go, combines planning and deep reinforcement
learning with self-play. We extend this approach to imperfect information games
and present ExIt-OOS, a novel approach to playing imperfect information games
within the Expert Iteration framework and inspired by AlphaZero. We use Online
Outcome Sampling, an online search algorithm for imperfect information games in
place of MCTS. While training online, our neural strategy is used to improve
the accuracy of playouts in OOS, allowing a learning and planning feedback loop
for imperfect information games.
