New from DeepLab · Mahjong Real-Time Analysis Tool Now Live

Deeplab’s New Product · Mahj Soul draws on research methods such as Deep CFR and CFR from the field of incomplete-information games to evaluate the potential rewards and risks of different decision paths through counterfactual analysis. At the same time, it incorporates deep reinforcement learning concepts to correlate hand states, candidate actions, and long-term rewards, helping the model learn strategic choices under various game situations. The policy network is used to predict candidate actions, while the value model evaluates the long-term value of different decisions, thereby forming a more comprehensive AI decision-making analysis framework. It conducts comprehensive analysis across dimensions such as offense, defense, reaching tenpai, tile efficiency, risk control, and expected score. When players face multiple play options, the AI can compare different strategies and, based on the current game situation, assess potential tile-draw efficiency, winning chances, and the risk of giving away a winning hand, providing customers with more intuitive decision-making guidance. During the initial launch phase, we are offering 1-on-1 team consultations and product demos. Please contact @DeepLabpro_bot on Telegram.