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item_updated_time: 1782623884866
title_diff: "[{\"diffs\":[[1,\"Gen 4 Strategy — Reinforcement Learning & AI\"]],\"start1\":0,\"start2\":0,\"length1\":0,\"length2\":44}]"
body_diff: "[{\"diffs\":[[1,\"# Gen 4 — Reinforcement Learning & AI-Driven Strategies\\\n\\\n> Extracted and consolidated from early architecture brainstorming.\\\n> These are long-term concepts, not yet implemented.\\\n\\\n---\\\n\\\n## Strategy Types\\\n\\\n### Reinforcement Learning (RL)\\\n- Train bots using self-play or against rule-based bots\\\n- Frameworks: Stable-Baselines3, Ray RLlib\\\n- Rust integration via tch-rs (PyTorch bindings) or ONNX Runtime\\\n\\\n### Supervised Learning\\\n- Train models on hand histories (PokerTracker DB)\\\n- Predict actions based on game state and opponent behavior\\\n- Encode game states (one-hot encoding for cards, normalize chip stacks)\\\n\\\n### Opponent Modeling (AI-driven)\\\n- Clustering or classification models to categorize opponents\\\n- Tight-aggressive, loose-passive, maniac, rock, etc.\\\n- Real-time adaptation based on observed behavior\\\n\\\n---\\\n\\\n## Training Pipeline\\\n\\\n### Data Collection\\\n- Simulate games using the testbed\\\n- Use existing hand histories from PokerTracker DB\\\n- Capture live game data via console listener / browser extension\\\n\\\n### Preprocessing\\\n- Encode game states (cards → one-hot or embedding, stacks → normalized)\\\n- Split data into training and validation sets\\\n- Feature engineering: hand strength, pot odds, position encoding\\\n\\\n### Model Training\\\n- PyTorch or TensorFlow for model training\\\n- RTX 3060 laptop for local training\\\n- Google Colab / HuggingFace for experimentation\\\n\\\n### Evaluation\\\n- Evaluate against rule-based bots (Gen 1-3) and human players\\\n- Metrics: win rate, profit per hand, decision accuracy, ITM rate\\\n\\\n### Deployment\\\n- Export trained models to ONNX or TorchScript\\\n- Load into Rust bot framework via tch-rs or tract (Rust ONNX runtime)\\\n- A/B test against current generation\\\n\\\n---\\\n\\\n## Table Recognition (Computer Vision)\\\n- Recognize table states (cards, chips, pot) from screenshots or video feeds\\\n- Models: YOLO or EfficientNet for object detection\\\n- Synthetic data or screenshots for training\\\n- Integration with browser extension for live play\\\n\\\n---\\\n\\\n## Tools & Frameworks\\\n\\\n| Tool | Purpose |\\\n|------|---------|\\\n| PyTorch / TensorFlow | Model training |\\\n| Stable-Baselines3 / Ray RLlib | Reinforcement learning |\\\n| OpenCV / YOLO | Table recognition |\\\n| tch-rs | PyTorch Rust bindings |\\\n| ONNX / TorchScript | Model serialization |\\\n| tract | Rust-native ONNX inference |\\\n| Docker | Containerization for deployment |\\\n\\\n## Hardware\\\n\\\n| Resource | Use |\\\n|----------|-----|\\\n| RTX 3060 Laptop | Local model training |\\\n| Dual Xeon Server | Mass simulations + deployment |\\\n| Cloud Server | Host game engine + bots |\"]],\"start1\":0,\"start2\":0,\"length1\":0,\"length2\":2510}]"
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updated_time: 2026-06-28T05:27:38.820Z
created_time: 2026-06-28T05:27:38.820Z
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