Live Training

# Live Training

Design for a real-time bot improvement pipeline that captures live game data and uses it to train models.

---

## System Architecture

```
Browser Game → Console Listener → Data Pipeline → Live Trainer → Bot Framework
                  (capture)        (process)      (train)       (deploy)
```

The loop runs continuously: the bot plays, captures results, trains on new data, and deploys the updated model.

---

## Console Listener

Captures text output from the browser game console and parses it into structured game events.

```rust
struct ConsoleListener {
    log_file: String,
}

impl ConsoleListener {
    fn start(&self) {
        let reader = BufReader::new(File::open(&self.log_file).unwrap());
        for line in reader.lines().flatten() {
            if let Some(event) = self.parse_event(&line) {
                self.forward_event(event);
            }
        }
    }

    fn parse_event(&self, line: &str) -> Option<GameEvent> {
        // Parse "Player 1 raises to 50" → GameEvent::Raise { player_id: 1, amount: 50.0 }
        // Parse "Player 2 folds" → GameEvent::Fold { player_id: 2 }
        // etc.
        None
    }
}

enum GameEvent {
    Fold { player_id: u32 },
    Call { player_id: u32 },
    Raise { player_id: u32, amount: f64 },
    Deal { card: Card },
    // etc.
}
```

---

## Data Pipeline

Converts raw game events into feature vectors and labels for training.

```rust
struct DataPipeline {
    events: Vec<GameEvent>,
}

impl DataPipeline {
    fn process_hand(&self, events: &[GameEvent]) -> TrainingSample {
        // Extract features: hand strength, pot odds, position, stack ratios, board texture
        // Extract label: the action that was taken (or the optimal action)
        // Return (FeatureVector, Label) pair
    }
}
```

---

## Live Training Framework

Continuously retrains models using the latest game data. Uses PyTorch via tch-rs or ONNX Runtime via tract.

```rust
struct LiveTrainer {
    model: Box<dyn Model>,
}

impl LiveTrainer {
    fn train(&mut self, batch: &[TrainingSample]) {
        for sample in batch {
            let loss = self.model.forward(&sample.features)
                .cross_entropy(&sample.label);
            self.model.backward_step(&loss);
        }
    }

    fn export_model(&self) -> Vec<u8> {
        // Serialize to ONNX or TorchScript for deployment
        self.model.serialize()
    }
}
```

---

## Deployment Loop

1. **Capture:** Console listener parses game events from browser
2. **Process:** Data pipeline converts events → training samples
3. **Train:** Live trainer updates model on new data
4. **Deploy:** Updated model loaded into strategy bot
5. **Act:** Bot uses new model for next decision
6. **Repeat**

---

## Integration Points

- **With browser extension:** Console listener reads from extension's game state output (see "Browser Extension" note)
- **With bot framework:** Trained model implements the `Strategy` trait and is loaded into `StrategyBot`
- **With simulation testbed:** Can pre-train on simulated hands before live deployment

---

## Implementation Considerations

- **Batch size:** Accumulate N hands before retraining to avoid noisy updates
- **Model versioning:** Keep previous model version for rollback if new one performs worse
- **A/B testing:** Run old and new models in parallel, compare results
- **Offline validation:** Validate updated model against simulation testbed before live deployment

id: 386b183ea4f44c2eac33a73292f24db1
parent_id: 2c8da247905946c3aa19eb4936e16323
created_time: 2025-02-13T05:51:04.270Z
updated_time: 2026-05-31T11:00:35.866Z
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