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item_id: 386b183ea4f44c2eac33a73292f24db1
item_updated_time: 1780225235866
title_diff: "[]"
body_diff: "[{\"diffs\":[[0,\"ng\\\n\\\n\"],[-1,\"## 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\\\nC\"],[1,\"Design 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 c\"],[0,\"onti\"]],\"start1\":13,\"start2\":13,\"length1\":406,\"length2\":320},{\"diffs\":[[0,\"usly\"],[1,\":\"],[0,\" t\"],[-1,\"rains 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 \"],[1,\"he 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_\"],[0,\"file\"],[-1,\"\\\");\\\n        let reader = BufReader::new(file\"],[1,\").unwrap()\"],[0,\");\\\n\"],[-1,\"\\\n\"],[0,\"    \"]],\"start1\":336,\"start2\":336,\"length1\":887,\"length2\":393},{\"diffs\":[[0,\"es()\"],[-1,\" {\\\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\"],[1,\".flatten() {\\\n    \"],[0,\"    \"]],\"start1\":755,\"start2\":755,\"length1\":206,\"length2\":25},{\"diffs\":[[0,\"e_event(\"],[1,\"&\"],[0,\"line) {\\\n\"]],\"start1\":814,\"start2\":814,\"length1\":16,\"length2\":17},{\"diffs\":[[0,\"    \"],[-1,\"        // Forward the event to the data pipeline\\\n\"],[0,\"    \"]],\"start1\":831,\"start2\":831,\"length1\":58,\"length2\":8},{\"diffs\":[[0,\"ent(event);\\\n\"],[1,\"            }\\\n\"],[0,\"        }\\\n  \"]],\"start1\":862,\"start2\":862,\"length1\":24,\"length2\":38},{\"diffs\":[[0,\"  //\"],[-1,\" Implement parsing logic here\\\n        // Example:\"],[0,\" Par\"]],\"start1\":972,\"start2\":972,\"length1\":57,\"length2\":8},{\"diffs\":[[0,\" to 50\\\" \"],[-1,\"into a\"],[1,\"→\"],[0,\" GameEve\"]],\"start1\":999,\"start2\":999,\"length1\":22,\"length2\":17},{\"diffs\":[[0,\"aise\"],[-1,\"\\\n        None\"],[1,\" { player_id: 1, amount: 50.0 }\"],[0,\"\\\n    \"],[-1,\"}\\\n\\\n\"],[0,\"    \"],[-1,\"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 \"],[1,\"// 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(&\"],[0,\"self\"]],\"start1\":1021,\"start2\":1021,\"length1\":717,\"length2\":506},{\"diffs\":[[0,\"f, event\"],[1,\"s\"],[0,\": \"],[1,\"&[\"],[0,\"GameEven\"]],\"start1\":1526,\"start2\":1526,\"length1\":18,\"length2\":21},{\"diffs\":[[0,\"vent\"],[1,\"]\"],[0,\") \"],[-1,\"{\\\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 c\"],[1,\"-> 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\\\nC\"],[0,\"onti\"]],\"start1\":1544,\"start2\":1544,\"length1\":536,\"length2\":292},{\"diffs\":[[0,\"k\\\n\\\nContinuously \"],[1,\"re\"],[0,\"trains models us\"]],\"start1\":1828,\"start2\":1828,\"length1\":32,\"length2\":34},{\"diffs\":[[0,\"ta. \"],[-1,\"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\"],[1,\"Uses PyTorch via tch-rs or ONNX Runtime via tract.\\\n\\\n```rust\\\nstruct LiveTrainer {\\\n    model: Box<dyn Model>,\\\n}\\\n\\\nimpl LiveTrainer {\"],[0,\"\\\n   \"]],\"start1\":1884,\"start2\":1884,\"length1\":429,\"length2\":137},{\"diffs\":[[0,\"lf, \"],[-1,\"data: Vec<(FeatureVector, Label)>) {\\\n        for (features, label)\"],[1,\"batch: &[TrainingSample]) {\\\n        for sample\"],[0,\" in \"],[-1,\"data\"],[1,\"batch\"],[0,\" {\\\n \"]],\"start1\":2038,\"start2\":2038,\"length1\":82,\"length2\":63},{\"diffs\":[[0,\"let \"],[-1,\"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\"],[1,\"loss = self.model.forward(&sample.features)\\\n                .cross_entropy(&sample.label\"],[0,\");\\\n\"],[-1,\"\\\n\"],[0,\"    \"]],\"start1\":2112,\"start2\":2112,\"length1\":206,\"length2\":99},{\"diffs\":[[0,\"   self.\"],[-1,\"optimizer\"],[1,\"model\"],[0,\".backwar\"]],\"start1\":2216,\"start2\":2216,\"length1\":25,\"length2\":21},{\"diffs\":[[0,\"  }\\\n\"],[-1,\"}\\\n\\\nstruct NeuralNetwork {\\\n    // Define your neural network here\\\n}\\\n\\\nimpl NeuralNetwork {\\\n    fn new() -> Self\"],[1,\"\\\n    fn export_model(&self) -> Vec<u8>\"],[0,\" {\\\n \"]],\"start1\":2264,\"start2\":2264,\"length1\":117,\"length2\":46},{\"diffs\":[[0,\" // \"],[-1,\"Init\"],[1,\"Ser\"],[0,\"ialize t\"],[-1,\"he 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###\"],[1,\"o ONNX or TorchScript for deployment\\\n        self.model.serialize()\\\n    }\\\n}\\\n```\\\n\\\n---\\\n\\\n## Deployment Loop\\\n\\\n1. **Capture:**\"],[0,\" Con\"]],\"start1\":2316,\"start2\":2316,\"length1\":458,\"length2\":140},{\"diffs\":[[0,\"Console \"],[-1,\"L\"],[1,\"l\"],[0,\"istener\"],[-1,\"\\\n\\\nCaptur\"],[1,\" pars\"],[0,\"es game \"]],\"start1\":2453,\"start2\":2453,\"length1\":32,\"length2\":29},{\"diffs\":[[0,\"nts \"],[-1,\"and forwards them to the data pipeline.\\\n\\\n### Data Pipeline\\\n\\\nProcesse\"],[1,\"from browser\\\n2. **Process:** Data pipeline convert\"],[0,\"s ev\"]],\"start1\":2485,\"start2\":2485,\"length1\":76,\"length2\":58},{\"diffs\":[[0,\" events \"],[-1,\"into\"],[1,\"→\"],[0,\" trainin\"]],\"start1\":2540,\"start2\":2540,\"length1\":20,\"length2\":17},{\"diffs\":[[0,\"ing \"],[-1,\"data.\\\n\\\n###\"],[1,\"samples\\\n3. **Train:**\"],[0,\" Live \"],[-1,\"T\"],[1,\"t\"],[0,\"rainer\"],[-1,\"\\\n\\\nContinuously trains models using the latest data.\\\n\\\n### Bot Framework\\\n\\\nUses the latest trained\"],[1,\" updates model on new data\\\n4. **Deploy:** Updated model loaded into strategy bot\\\n5. **Act:** Bot uses new\"],[0,\" model\"],[-1,\"s 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 forwar\"],[1,\" 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 loa\"],[0,\"ded \"],[1,\"in\"],[0,\"to \"],[-1,\"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\"],[1,\"`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\":2555,\"start2\":2555,\"length1\":524,\"length2\":900}]"
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updated_time: 2026-05-31T11:06:30.557Z
created_time: 2026-05-31T11:06:30.557Z
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