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item_type: 1
item_id: 386b183ea4f44c2eac33a73292f24db1
item_updated_time: 1780225235865
title_diff: "[{\"diffs\":[[1,\"Live Training\"]],\"start1\":0,\"start2\":0,\"length1\":0,\"length2\":13}]"
body_diff: "[{\"diffs\":[[1,\"# Live Training\\\n\\\n## System Architecture for Live Training\\\n### Console Listener\\\n\\\nCaptures text output from the browser game's console.\\\n\\\nParses the text into structured game events (e.g., player actions, game state updates).\\\n\\\n### Data Pipeline\\\n\\\nProcesses and stores game events for training.\\\n\\\nConverts raw text into a format suitable for model training (e.g., feature vectors, labels).\\\n\\\n### Live Training Framework\\\n\\\nContinuously trains models using the latest game data.\\\n\\\nUpdates models in real-time or at regular intervals.\\\n\\\n### Bot Framework\\\n\\\nUses the latest trained models to make decisions.\\\n\\\nInteracts with the browser game via the console or other input methods.\\\n\\\n## Console Listener\\\nThe console listener captures and parses text output from the browser game. This can be done using Rust's file I/O and string processing capabilities.\\\n\\\n### Implementation\\\n\\\n`use std::fs::File;\\\nuse std::io::{BufRead, BufReader};\\\nuse std::path::Path;\\\n\\\nstruct ConsoleListener {\\\n    log_file: String,\\\n}\\\n\\\nimpl ConsoleListener {\\\n    fn new(log_file: &str) -> Self {\\\n        Self {\\\n            log_file: log_file.to_string(),\\\n        }\\\n    }\\\n\\\n    fn start(&self) {\\\n        let path = Path::new(&self.log_file);\\\n        let file = File::open(path).expect(\\\"Failed to open log file\\\");\\\n        let reader = BufReader::new(file);\\\n\\\n        for line in reader.lines() {\\\n            if let Ok(line) = line {\\\n                self.process_line(&line);\\\n            }\\\n        }\\\n    }\\\n\\\n    fn process_line(&self, line: &str) {\\\n        // Parse the line into a game event\\\n        if let Some(event) = self.parse_event(line) {\\\n            // Forward the event to the data pipeline\\\n            self.forward_event(event);\\\n        }\\\n    }\\\n\\\n    fn parse_event(&self, line: &str) -> Option<GameEvent> {\\\n        // Implement parsing logic here\\\n        // Example: Parse \\\"Player 1 raises to 50\\\" into a GameEvent::Raise\\\n        None\\\n    }\\\n\\\n    fn forward_event(&self, event: GameEvent) {\\\n        // Send the event to the data pipeline\\\n        println!(\\\"Forwarding event: {:?}\\\", event);\\\n    }\\\n}\\\n\\\n#[derive(Debug)]\\\nenum GameEvent {\\\n    Fold { player_id: u32 },\\\n    Call { player_id: u32 },\\\n    Raise { player_id: u32, amount: f64 },\\\n    // Add other event types...\\\n}`\\\n\\\n## Data Pipeline\\\nThe data pipeline processes and stores game events for training. It converts raw events into feature vectors and labels for supervised or reinforcement learning.\\\n\\\n### Implementation\\\n\\\n`struct DataPipeline {\\\n    events: Vec<GameEvent>,\\\n}\\\n\\\nimpl DataPipeline {\\\n    fn new() -> Self {\\\n        Self { events: Vec::new() }\\\n    }\\\n\\\n    fn add_event(&mut self, event: GameEvent) {\\\n        self.events.push(event);\\\n    }\\\n\\\n    fn process_events(&self) -> Vec<(FeatureVector, Label)> {\\\n        // Convert events into feature vectors and labels\\\n        self.events.iter()\\\n            .map(|event| self.event_to_training_data(event))\\\n            .collect()\\\n    }\\\n\\\n    fn event_to_training_data(&self, event: &GameEvent) -> (FeatureVector, Label) {\\\n        // Implement conversion logic here\\\n        (FeatureVector::default(), Label::default())\\\n    }\\\n}`\\\n\\\n## Live Training Framework\\\nThe live training framework continuously trains models using the latest game data. It can use frameworks like PyTorch or TensorFlow via Rust bindings (e.g., tch-rs).\\\n\\\n### Implementation\\\n\\\n`use tch::nn::{Module, OptimizerConfig};\\\nuse tch::{Device, Tensor};\\\n\\\nstruct LiveTrainer {\\\n    model: NeuralNetwork,\\\n    optimizer: Optimizer,\\\n}\\\n\\\nimpl LiveTrainer {\\\n    fn new() -> Self {\\\n        let model = NeuralNetwork::new();\\\n        let optimizer = Optimizer::new(&model);\\\n        Self { model, optimizer }\\\n    }\\\n\\\n    fn train(&mut self, data: Vec<(FeatureVector, Label)>) {\\\n        for (features, label) in data {\\\n            let input = Tensor::from(features);\\\n            let target = Tensor::from(label);\\\n\\\n            let output = self.model.forward(&input);\\\n            let loss = output.cross_entropy_for_logits(&target);\\\n\\\n            self.optimizer.backward_step(&loss);\\\n        }\\\n    }\\\n}\\\n\\\nstruct NeuralNetwork {\\\n    // Define your neural network here\\\n}\\\n\\\nimpl NeuralNetwork {\\\n    fn new() -> Self {\\\n        // Initialize the network\\\n        Self {}\\\n    }\\\n\\\n    fn forward(&self, input: &Tensor) -> Tensor {\\\n        // Implement forward pass\\\n        input.clone()\\\n    }\\\n}\\\n\\\nstruct Optimizer {\\\n    // Define your optimizer here\\\n}\\\n\\\nimpl Optimizer {\\\n    fn new(model: &NeuralNetwork) -> Self {\\\n        // Initialize the optimizer\\\n        Self {}\\\n    }\\\n\\\n    fn backward_step(&mut self, loss: &Tensor) {\\\n        // Implement backward pass\\\n    }\\\n}`\\\n\\\n## Integration\\\n\\\n### Console Listener\\\n\\\nCaptures game events and forwards them to the data pipeline.\\\n\\\n### Data Pipeline\\\n\\\nProcesses events into training data.\\\n\\\n### Live Trainer\\\n\\\nContinuously trains models using the latest data.\\\n\\\n### Bot Framework\\\n\\\nUses the latest trained models to make decisions.\\\n\\\n## Example Workflow\\\nThe console listener captures a line: \\\"Player 1 raises to 50\\\".\\\n\\\nThe listener parses the line into a GameEvent::Raise.\\\n\\\nThe event is forwarded to the data pipeline.\\\n\\\nThe data pipeline converts the event into a feature vector and label.\\\n\\\nThe live trainer updates the model using the new data.\\\n\\\nThe bot uses the updated model to make decisions in the game.\\\n\"]],\"start1\":0,\"start2\":0,\"length1\":0,\"length2\":5214}]"
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updated_time: 2026-05-31T11:06:30.239Z
created_time: 2026-05-31T11:06:30.239Z
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