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Transformer Based Planning in the Observation Space with Applications to Trick Taking Card Games
Traditional search algorithms have issues when applied to games of imperfect information where the number of possible underlying states and trajectories are very large.
Policy Based Inference in Trick-Taking Card Games
Trick-taking card games feature a large amount of private information that slowly gets revealed through a long sequence of actions.
Learning Policies from Human Data for Skat
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.
Improving Search with Supervised Learning in Trick-Based Card Games
In trick-taking card games, a two-step process of state sampling and evaluation is widely used to approximate move values.
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