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This tutorial explains how to create an AI‑driven Dou Dizhu (Chinese poker) assistant that captures game screenshots, uses YOLOv5 for card detection, leverages the DouZero model for optimal move prediction, and provides a PyQt5 UI for real‑time play assistance, including environment setup and code examples.
This article explains how to create an AI‑driven Dou Dizhu card‑playing assistant by integrating the open‑source DouZero model with a PyQt5 GUI, covering UI layout, hand‑card and role recognition via screen capture, AI decision making, and step‑by‑step deployment instructions.
DouZero, a reinforcement‑learning AI for the Chinese card game Dou Dizhu, combines a Monte‑Carlo value‑network with compact action encoding, trains on a four‑GPU server, and outperforms existing AI baselines, ranks first on Botzone, and even surpasses human play in several metrics.
The paper presents DouZero, a reinforcement‑learning AI for the Chinese card game Dou Dizhu that combines a Monte‑Carlo method with a value network, uses binary matrix encodings for states and actions, and achieves human‑level play and state‑of‑the‑art results on modest GPU hardware.