Using self-play, MCTS, and a deep neural network to create a hearthstone ai player
A modern C++14 header-only template library implementing information set Monte Carlo tree search.
Headless C# card-game engine + AI research framework: heuristic, ISMCTS, and an AlphaZero-style self-play NN+MCTS agent, benchmarked by a reproducible Elo ladder under imperfect information.
Implementacija Preferansa u Pythonu sa AI agentima — heuristički bot, napredni bot i ISMCTS (Information Set Monte Carlo Tree Search) za igru sa nepotpunim informacijama.
Domino Poker MAX BOT - A strong hidden-information game AI for 4-player Domino Poker — built without any neural network. It reaches its strength purely from search, Monte-Carlo simulation, Bayesian inference, and online opponent modeling. Monte-Carlo Tree Search (ISMCTS), Determinization + Bayesian re-weighting.
Движок «Дурака» на Rust — ISMCTS с детерминизацией + решатель эндшпиля, байес-модель оппонента, параллельный поиск; веб-GUI и CLI
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