Gen 4 Strategy — Reinforcement Learning & AI

# Gen 4 — Reinforcement Learning & AI-Driven Strategies

> Extracted and consolidated from early architecture brainstorming.
> These are long-term concepts, not yet implemented.

---

## Strategy Types

### Reinforcement Learning (RL)
- Train bots using self-play or against rule-based bots
- Frameworks: Stable-Baselines3, Ray RLlib
- Rust integration via tch-rs (PyTorch bindings) or ONNX Runtime

### Supervised Learning
- Train models on hand histories (PokerTracker DB)
- Predict actions based on game state and opponent behavior
- Encode game states (one-hot encoding for cards, normalize chip stacks)

### Opponent Modeling (AI-driven)
- Clustering or classification models to categorize opponents
- Tight-aggressive, loose-passive, maniac, rock, etc.
- Real-time adaptation based on observed behavior

---

## Training Pipeline

### Data Collection
- Simulate games using the testbed
- Use existing hand histories from PokerTracker DB
- Capture live game data via console listener / browser extension

### Preprocessing
- Encode game states (cards → one-hot or embedding, stacks → normalized)
- Split data into training and validation sets
- Feature engineering: hand strength, pot odds, position encoding

### Model Training
- PyTorch or TensorFlow for model training
- RTX 3060 laptop for local training
- Google Colab / HuggingFace for experimentation

### Evaluation
- Evaluate against rule-based bots (Gen 1-3) and human players
- Metrics: win rate, profit per hand, decision accuracy, ITM rate

### Deployment
- Export trained models to ONNX or TorchScript
- Load into Rust bot framework via tch-rs or tract (Rust ONNX runtime)
- A/B test against current generation

---

## Table Recognition (Computer Vision)
- Recognize table states (cards, chips, pot) from screenshots or video feeds
- Models: YOLO or EfficientNet for object detection
- Synthetic data or screenshots for training
- Integration with browser extension for live play

---

## Tools & Frameworks

| Tool | Purpose |
|------|---------|
| PyTorch / TensorFlow | Model training |
| Stable-Baselines3 / Ray RLlib | Reinforcement learning |
| OpenCV / YOLO | Table recognition |
| tch-rs | PyTorch Rust bindings |
| ONNX / TorchScript | Model serialization |
| tract | Rust-native ONNX inference |
| Docker | Containerization for deployment |

## Hardware

| Resource | Use |
|----------|-----|
| RTX 3060 Laptop | Local model training |
| Dual Xeon Server | Mass simulations + deployment |
| Cloud Server | Host game engine + bots |

id: dcce2cf3990f48809801a7e7f674c18a
parent_id: e13f1845de9b4b6392ad866354fbd562
created_time: 2026-05-31T10:53:23.747Z
updated_time: 2026-05-31T10:53:23.747Z
is_conflict: 0
latitude: 0.00000000
longitude: 0.00000000
altitude: 0.0000
author: 
source_url: 
is_todo: 0
todo_due: 0
todo_completed: 0
source: joplin-desktop
source_application: net.cozic.joplin-desktop
application_data: 
order: 1780224803747
user_created_time: 2026-05-31T10:53:23.747Z
user_updated_time: 2026-05-31T10:53:23.747Z
encryption_cipher_text: 
encryption_applied: 0
markup_language: 1
is_shared: 0
share_id: 
conflict_original_id: 
master_key_id: 
user_data: 
deleted_time: 0
type_: 1