QE Learning Optimization

QE Learning Optimization

Purpose

Guide the use of v3's learning optimization capabilities including transfer learning between agents, hyperparameter tuning, A/B testing, and continuous performance improvement.

Activation

• When optimizing agent performance
• When transferring knowledge between agents
• When tuning learning parameters
• When running A/B tests
• When analyzing learning metrics

Quick Start

# Transfer knowledge between agents
aqe learn transfer --from jest-generator --to vitest-generator

# Tune hyperparameters
aqe learn tune --agent defect-predictor --metric accuracy

# Run A/B test
aqe learn ab-test --hypothesis "new-algorithm" --duration 7d

# View learning metrics
aqe learn metrics --agent test-generator --period 30d

Agent Workflow

// Transfer learning
Task("Transfer test patterns", `
  Transfer learned patterns from Jest test generator to Vitest:
  - Map framework-specific syntax
  - Adapt assertion styles
  - Preserve test structure patterns
  - Validate transfer accuracy
`, "qe-transfer-specialist")

// Metrics optimization
Task("Optimize prediction accuracy", `
  Tune defect-predictor agent:
  - Analyze current performance metrics
  - Run Bayesian hyperparameter search
  - Validate improvements on holdout set
  - Deploy if accuracy improves >5%
`, "qe-metrics-optimizer")

Learning Operations

1. Transfer Learning

await transferSpecialist.transfer({
  source: {
    agent: 'qe-jest-generator',
    knowledge: ['patterns', 'heuristics', 'optimizations']
  },
  target: {
    agent: 'qe-vitest-generator',
    adaptations: ['framework-syntax', 'api-differences']
  },
  strategy: 'fine-tuning',
  validation: {
    testSet: 'validation-samples',
    minAccuracy: 0.9
  }
});

2. Hyperparameter Tuning

await metricsOptimizer.tune({
  agent: 'defect-predictor',
  parameters: {
    learningRate: { min: 0.001, max: 0.1, type: 'log' },
    batchSize: { values: [16, 32, 64, 128] },
    patternThreshold: { min: 0.5, max: 0.95 }
  },
  optimization: {
    method: 'bayesian',
    objective: 'accuracy',
    trials: 50,
    parallelism: 4
  }
});