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item_updated_time: 1784277453210
title_diff: "[{\"diffs\":[[1,\"Live Training\"]],\"start1\":0,\"start2\":0,\"length1\":0,\"length2\":13}]"
body_diff: "[{\"diffs\":[[1,\"# Live Training\\\n\\\nDesign for a real-time bot improvement pipeline that captures live game data and uses it to train models.\\\n\\\n---\\\n\\\n## System Architecture\\\n\\\n```\\\nBrowser Game → Console Listener → Data Pipeline → Live Trainer → Bot Framework\\\n                  (capture)        (process)      (train)       (deploy)\\\n```\\\n\\\nThe loop runs continuously: the bot plays, captures results, trains on new data, and deploys the updated model.\\\n\\\n---\\\n\\\n## Console Listener\\\n\\\nCaptures text output from the browser game console and parses it into structured game events.\\\n\\\n```rust\\\nstruct ConsoleListener {\\\n    log_file: String,\\\n}\\\n\\\nimpl ConsoleListener {\\\n    fn start(&self) {\\\n        let reader = BufReader::new(File::open(&self.log_file).unwrap());\\\n        for line in reader.lines().flatten() {\\\n            if let Some(event) = self.parse_event(&line) {\\\n                self.forward_event(event);\\\n            }\\\n        }\\\n    }\\\n\\\n    fn parse_event(&self, line: &str) -> Option<GameEvent> {\\\n        // Parse \\\"Player 1 raises to 50\\\" → GameEvent::Raise { player_id: 1, amount: 50.0 }\\\n        // Parse \\\"Player 2 folds\\\" → GameEvent::Fold { player_id: 2 }\\\n        // etc.\\\n        None\\\n    }\\\n}\\\n\\\nenum GameEvent {\\\n    Fold { player_id: u32 },\\\n    Call { player_id: u32 },\\\n    Raise { player_id: u32, amount: f64 },\\\n    Deal { card: Card },\\\n    // etc.\\\n}\\\n```\\\n\\\n---\\\n\\\n## Data Pipeline\\\n\\\nConverts raw game events into feature vectors and labels for training.\\\n\\\n```rust\\\nstruct DataPipeline {\\\n    events: Vec<GameEvent>,\\\n}\\\n\\\nimpl DataPipeline {\\\n    fn process_hand(&self, events: &[GameEvent]) -> TrainingSample {\\\n        // Extract features: hand strength, pot odds, position, stack ratios, board texture\\\n        // Extract label: the action that was taken (or the optimal action)\\\n        // Return (FeatureVector, Label) pair\\\n    }\\\n}\\\n```\\\n\\\n---\\\n\\\n## Live Training Framework\\\n\\\nContinuously retrains models using the latest game data. Uses PyTorch via tch-rs or ONNX Runtime via tract.\\\n\\\n```rust\\\nstruct LiveTrainer {\\\n    model: Box<dyn Model>,\\\n}\\\n\\\nimpl LiveTrainer {\\\n    fn train(&mut self, batch: &[TrainingSample]) {\\\n        for sample in batch {\\\n            let loss = self.model.forward(&sample.features)\\\n                .cross_entropy(&sample.label);\\\n            self.model.backward_step(&loss);\\\n        }\\\n    }\\\n\\\n    fn export_model(&self) -> Vec<u8> {\\\n        // Serialize to ONNX or TorchScript for deployment\\\n        self.model.serialize()\\\n    }\\\n}\\\n```\\\n\\\n---\\\n\\\n## Deployment Loop\\\n\\\n1. **Capture:** Console listener parses game events from browser\\\n2. **Process:** Data pipeline converts events → training samples\\\n3. **Train:** Live trainer updates model on new data\\\n4. **Deploy:** Updated model loaded into strategy bot\\\n5. **Act:** Bot uses new model for next decision\\\n6. **Repeat**\\\n\\\n---\\\n\\\n## Integration Points\\\n\\\n- **With browser extension:** Console listener reads from extension's game state output (see \\\"Browser Extension\\\" note)\\\n- **With bot framework:** Trained model implements the `Strategy` trait and is loaded into `StrategyBot`\\\n- **With simulation testbed:** Can pre-train on simulated hands before live deployment\\\n\\\n---\\\n\\\n## Implementation Considerations\\\n\\\n- **Batch size:** Accumulate N hands before retraining to avoid noisy updates\\\n- **Model versioning:** Keep previous model version for rollback if new one performs worse\\\n- **A/B testing:** Run old and new models in parallel, compare results\\\n- **Offline validation:** Validate updated model against simulation testbed before live deployment\"]],\"start1\":0,\"start2\":0,\"length1\":0,\"length2\":3455}]"
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