d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
65 lines
2.3 KiB
Rust
65 lines
2.3 KiB
Rust
//! Distributed Learning Example
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//!
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//! Demonstrates distributed Q-learning across multiple agents.
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use ruvector_edge::prelude::*;
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use ruvector_edge::IntelligenceSync;
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use std::sync::Arc;
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use tokio::sync::RwLock;
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#[tokio::main]
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async fn main() -> Result<()> {
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tracing_subscriber::fmt()
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.with_env_filter("info")
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.init();
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println!("Distributed Learning Example");
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println!("============================\n");
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// Create intelligence sync for aggregated learning
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let sync = Arc::new(RwLock::new(IntelligenceSync::new("swarm-coordinator")));
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// Simulate multiple learning agents with their own experiences
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let scenarios = vec![
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("learner-001", "edit_code", "coder", 0.9),
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("learner-001", "review_code", "reviewer", 0.85),
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("learner-002", "test_code", "tester", 0.88),
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("learner-002", "debug_error", "debugger", 0.92),
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("learner-003", "deploy_app", "devops", 0.87),
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("learner-003", "edit_code", "coder", 0.95), // Another agent learns edit_code
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];
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println!("Distributed learning phase:");
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for (agent, state, action, reward) in &scenarios {
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let sync_guard = sync.write().await;
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sync_guard.update_pattern(state, action, *reward);
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println!(" {} learned: {} -> {} ({:.2})", agent, state, action, reward);
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}
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// Query merged intelligence
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let sync_guard = sync.read().await;
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let states_to_query = vec!["edit_code", "review_code", "test_code", "debug_error", "deploy_app"];
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println!("\nMerged intelligence queries:");
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for state in states_to_query {
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if let Some((action, confidence)) = sync_guard.get_best_action(
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state,
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&["coder", "reviewer", "tester", "debugger", "devops"]
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.iter().map(|s| s.to_string()).collect::<Vec<_>>()
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) {
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println!(" {} -> {} (confidence: {:.1}%)", state, action, confidence * 100.0);
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}
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}
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// Get swarm stats
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let stats = sync_guard.get_swarm_stats();
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println!("\nSwarm statistics:");
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println!(" Total patterns: {}", stats.total_patterns);
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println!(" Total visits: {}", stats.total_visits);
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println!(" Avg confidence: {:.1}%", stats.avg_confidence * 100.0);
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println!("\nDistributed learning example complete!");
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Ok(())
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}
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