  We consider learning to play multiplayer imperfect-information games with
simultaneous moves and large state-action spaces. Previous attempts to tackle
such challenging games have largely focused on model-free learning methods,
often requiring hundreds of years of experience to produce competitive agents.
Our approach is based on model-based planning. We tackle the problem of partial
observability by first building an (oracle) planner that has access to the full
state of the environment and then distilling the knowledge of the oracle to a
(follower) agent which is trained to play the imperfect-information game by
imitating the oracle's choices. We experimentally show that planning with naive
Monte Carlo tree search does not perform very well in large combinatorial
action spaces. We therefore propose planning with a fixed-depth tree search and
decoupled Thompson sampling for action selection. We show that the planner is
able to discover efficient playing strategies in the games of Clash Royale and
Pommerman and the follower policy successfully learns to implement them by
training on a few hundred battles.
