Scalable Decision Making for Games of Imperfect Information

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arXiv:2511.07312v2 Announce Type: replace-cross Abstract: Real-world decision-making generally involves hidden information, that is, information that is unknown to one agent but possessed by another. Unfortunately, the presence of large amounts of hidden information renders established reinforcement learning and search approaches ineffective. Even with multimillion-dollar industrial research efforts, top-human-level play at Stratego---a board wargame with hidden information on a massive scale---