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Decision-making in large imperfect information games is difficult. Thanks torecent success in Poker, Counterfactual Regret Minimization (CFR) methods havebeen at the forefront of research in these games. However, most of the successin large games comes with the use of a forward model and powerful stateabstractions. In trick-taking card games like Bridge or Skat, large informationsets and an inability to advance the simulation without fully determinizing thestate make forward search problematic. Furthermore, state abstractions can beespecially difficult to construct because the precise holdings of each playerdirectly impact move values.  In this paper we explore learning model-free policies for Skat from humangame data using deep neural networks (DNN). We produce a new state-of-the-artsystem for bidding and game declaration by introducing methods to a) directlyvary the aggressiveness of the bidder and b) declare games based on expectedvalue while mitigating issues with rarely observed state-action pairs. Althoughcardplay policies learned through imitation are slightly weaker than thecurrent best search-based method, they run orders of magnitude faster. We alsoexplore how these policies could be learned directly from experience in areinforcement learning setting and discuss the value of incorporating humandata for this task.

Learning Policies from Human Data for Skat