Czy AI może pokonać najlepszych ludzi na świecie w heads-up no-limit poker ?

Libratus pokonał czołowych profesjonalistów w 120 000 rozdań w kasynie Rivers w styczniu 2017 roku. Był to pierwszy raz, gdy AI wyraźnie przewyższyła ludzi w grach z niepełną informacją na dużą skalę.

Background

Libratus’s victory over top professionals at Rivers Casino in January 2017 became the first unambiguous demonstration that an AI could surpass humans in large-scale, imperfect-information games like heads-up no-limit Texas hold’em (Brown & Sandholm, 2017). Prior to Libratus, poker-playing systems such as Cepheus had demonstrated strong performance in the smaller heads-up limit variant, effectively playing an equilibrium strategy that ties even against human experts (Bowling et al., 2015, Science). Libratus advanced the state of the art by combining blueprint and real-time equilibrium-finding algorithms with a self-improved endgame solver that adapted strategy across consecutive matches rather than relying solely on precomputed strategies (Brown et al., 2018, AAAI). The 20-day tournament at Rivers Casino featured 120,000 hands against four elite human opponents—Dong Kim, Jason Les, Daniel McAulay, and Jimmy Chou—and Libratus accumulated more than $1.7 million in chips, a margin that statistical analysis subsequently confirmed as statistically significant beyond the margin of human counter-variance (Science, 2017). Analysts attribute Libratus’s breakthrough to its three-component architecture: a precomputed blueprint strategy for early rounds, a nested subgame solver for later, information-constrained portions of the game tree, and a self-refinement loop that updated its blueprint after each day of play (Carnegie Mellon University press release, 2017; Sandholm, 2017, MIT Technology Review). Subsequent AI systems such as Pluribus further extended the victory to multi-player no-limit hold’em by incorporating equilibrium approximation and decentralized self-play learning (Brown & Sandholm, 2019, Science).