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item_id: 66428ce0e55d424eb7f1522468c17cb7
item_updated_time: 1780224803784
title_diff: "[{\"diffs\":[[1,\"Architecture\"]],\"start1\":0,\"start2\":0,\"length1\":0,\"length2\":12}]"
body_diff: "[{\"diffs\":[[1,\"# Architecture\\\n\\\n## Core Components\\\n### Game Engine\\\n\\\nHandles the rules of Texas Hold'em (e.g., dealing cards, managing bets, determining winners).\\\n\\\nManages the game state (e.g., community cards, player stacks, pot size).\\\n\\\nWritten in Rust for performance and safety.\\\n\\\n### Bot Framework\\\n\\\nModular design for strategies (e.g., pre-flop, post-flop, heads-up).\\\n\\\nEach strategy module can be rule-based, statistical, or AI-driven.\\\n\\\nInterfaces with the game engine to receive game state and output actions (e.g., fold, call, raise).\\\n\\\n### AI Models\\\n\\\nDecision-Making Models: Neural networks or reinforcement learning (RL) models for decision-making in different game phases.\\\n\\\nOpponent Modeling: Models to predict opponents' behavior based on historical data (e.g., PokerTracker DB).\\\n\\\nTable Recognition: Computer vision models to recognize table states (e.g., cards, chips) from screenshots or video feeds.\\\n\\\n### Data Pipeline\\\n\\\nCollects game data (e.g., hand histories, player actions) for training and analysis.\\\n\\\nPreprocesses data for training AI models.\\\n\\\n### Training Framework\\\n\\\nUses frameworks like PyTorch or TensorFlow for training AI models.\\\n\\\nIntegrates with Rust via bindings (e.g., tch-rs for PyTorch).\\\n\\\n### Deployment\\\n\\\nDeploys trained models to the bot framework.\\\n\\\nSupports cloud (Debian Linux) and local (FreeBSD, Windows) environments.\\\n\\\n## AI-Driven Modular Strategies\\\n\\\n### Pre-Flop Strategy\\\n\\\nUses hand strength charts and opponent modeling.\\\n\\\nAI model trained on pre-flop decision-making (e.g., fold, call, raise based on position and hand strength).\\\n\\\n### Post-Flop Strategy\\\n\\\nEvaluates hand strength relative to community cards.\\\n\\\nAI model trained on post-flop decision-making (e.g., bluffing, value betting).\\\n\\\n### Heads-Up Strategy\\\n\\\nSpecialized strategy for two-player games.\\\n\\\nAI model trained on heads-up scenarios.\\\n\\\n### End-Game Strategy (SNG)\\\n\\\nFocuses on tournament-specific strategies (e.g., ICM considerations).\\\n\\\nAI model trained on end-game scenarios.\\\n\\\n### Reinforcement Learning (RL)\\\n\\\nTrain bots using self-play or against rule-based bots.\\\n\\\nUse frameworks like Stable-Baselines3 or Ray RLlib.\\\n\\\n### Supervised Learning\\\n\\\nTrain models on hand histories from PokerTracker DB.\\\n\\\nPredict actions based on game state and opponent behavior.\\\n\\\n### Opponent Modeling\\\n\\\nUse clustering or classification models to categorize opponents (e.g., tight-aggressive, loose-passive).\\\n\\\n## Pipelines for AI-Driven Strategies\\\nTraining Pipeline\\\nData Collection:\\\n\\\nSimulate games or use existing hand histories.\\\n\\\n### Preprocessing\\\n\\\nEncode game states (e.g., one-hot encoding for cards, normalize chip stacks).\\\n\\\nSplit data into training and validation sets.\\\n\\\n### Model Training\\\n\\\nTrain models using PyTorch or TensorFlow.\\\n\\\nUse Google Colab or your RTX 3060 laptop for training.\\\n\\\n### Evaluation\\\n\\\nEvaluate models against rule-based bots or human players.\\\n\\\nUse metrics like win rate, profit per hand, and decision accuracy.\\\n\\\n### Deployment Pipeline\\\n\\\nExport trained models to ONNX or TorchScript for interoperability.\\\n\\\nLoad models into the Rust bot framework using bindings (e.g., tch-rs).\\\n\\\nTest bots in simulated and real games.\\\n\\\nCollect performance data for retraining and improvement.\"]],\"start1\":0,\"start2\":0,\"length1\":0,\"length2\":3156}]"
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updated_time: 2026-05-31T10:56:29.045Z
created_time: 2026-05-31T10:56:29.045Z
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