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L2E: Learning to Exploit Your Opponent
In this work, we propose a novel Learning to Exploit (L2E) framework for implicit opponent modeling.
Temperature Regret Matching for Imperfect-Information Games
Regret matching (RM) plays a crucial role in CFR and its variants to approach Nash equilibrium.
OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed for large-scale imperfect-information game research.
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