  Decision-making in large imperfect information games is difficult. Thanks to
recent success in Poker, Counterfactual Regret Minimization (CFR) methods have
been at the forefront of research in these games. However, most of the success
in large games comes with the use of a forward model and powerful state
abstractions. In trick-taking card games like Bridge or Skat, large information
sets and an inability to advance the simulation without fully determinizing the
state make forward search problematic. Furthermore, state abstractions can be
especially difficult to construct because the precise holdings of each player
directly impact move values.
  In this paper we explore learning model-free policies for Skat from human
game data using deep neural networks (DNN). We produce a new state-of-the-art
system for bidding and game declaration by introducing methods to a) directly
vary the aggressiveness of the bidder and b) declare games based on expected
value while mitigating issues with rarely observed state-action pairs. Although
cardplay policies learned through imitation are slightly weaker than the
current best search-based method, they run orders of magnitude faster. We also
explore how these policies could be learned directly from experience in a
reinforcement learning setting and discuss the value of incorporating human
data for this task.
