proffesor-for-testing__agentic-qe/docs/neural-training-guide.md

Neural Training Guide - AgentDB RL Algorithms

Version: 1.2.0 Status: ✅ Production Ready Integration: AgentDB Learning Plugins

Overview

The AQE Fleet now supports 9 reinforcement learning algorithms via AgentDB integration for continuous agent improvement through neural training.

Supported RL Algorithms

Algorithm	Type	Best For	Description
Decision Transformer	Sequence Modeling	Long-term planning	Uses transformer architecture for sequential decision making
Q-Learning	Value-based (off-policy)	Discrete actions	Classic RL algorithm, learns optimal action-value function
SARSA	Value-based (on-policy)	Safe exploration	On-policy TD learning, more conservative than Q-learning
Actor-Critic	Policy gradient	Balanced performance	Combines value and policy learning for stability
PPO	Policy gradient	Stable training	Proximal Policy Optimization, industry standard for stability
DDPG	Continuous control	Continuous actions	Deep Deterministic Policy Gradient for continuous action spaces
TD3	Continuous control	Improved stability	Twin Delayed DDPG with enhanced stability
SAC	Maximum entropy	Exploration	Soft Actor-Critic, maximizes entropy for better exploration
DQN	Deep Q-Network	Complex states	Deep learning + Q-learning for high-dimensional states

Quick Start

1. Basic Neural Training

import { NeuralTrainer } from 'agentic-qe/core/neural';
import { createAgentDBManager } from 'agentic-qe/core/memory';

// Initialize AgentDB with learning enabled
const agentDB = createAgentDBManager({
  dbPath: '.agentdb/reasoningbank.db',
  enableLearning: true,
  enableReasoning: true
});
await agentDB.initialize();

// Create neural trainer
const trainer = new NeuralTrainer(
  'my-agent',
  memoryStore,
  agentDB,
  {
    enabled: true,
    algorithm: 'actor-critic',
    learningRate: 0.001,
    epochs: 50,
    batchSize: 32
  }
);
await trainer.initialize();

// Collect experiences during task execution
const experiences: Experience[] = [
  {
    state: { taskComplexity: 0.7, capabilities: ['test-gen'] },
    action: { type: 'parallel', parameters: {} },
    reward: 1.5,
    nextState: { taskComplexity: 0.7, capabilities: ['test-gen'] },
    done: true
  }
];

// Train the model
const result = await trainer.train(experiences);
console.log(`Loss: ${result.metrics.loss}, Episodes: ${result.episodeCount}`);

// Predict best action
const prediction = await trainer.predictAction({ taskComplexity: 0.6 });
console.log(`Best action: ${prediction.action.type}, confidence: ${prediction.confidence}`);

2. Using NeuralAgentExtension

For easier integration with existing agents:

import { NeuralAgentExtension } from 'agentic-qe/agents';

class TestGeneratorAgent extends BaseAgent {
  private neuralExt: NeuralAgentExtension;

  constructor(config: BaseAgentConfig) {
    super(config);

    // Add neural training extension
    this.neuralExt = new NeuralAgentExtension(
      this.agentId.id,
      this.memoryStore as SwarmMemoryManager,
      this.agentDB!,
      {
        enabled: true,
        algorithm: 'ppo', // Use PPO for stable training
        learningRate: 0.001
      },
      {
        enabled: true,
        collectionInterval: 1, // Collect every task
        maxBuffer: 1000,
        autoTrain: true // Auto-train when buffer full
      }
    );
  }

  protected async initializeComponents() {
    await this.neuralExt.initialize();
  }

  protected async onPreTask(data: PreTaskData) {
    await super.onPreTask(data);

    // Snapshot state before execution
    this.neuralExt.snapshotState(data.assignment.task);

    // Get action recommendation
    const state = this.extractState(data);
    const prediction = await this.neuralExt.predictAction(state);
    console.log(`Neural recommends: ${prediction.action.type} (confidence: ${prediction.confidence})`);
  }

  protected async onPostTask(data: PostTaskData) {
    await super.onPostTask(data);

    // Automatically collect experience
    await this.neuralExt.collectExperience(data);
  }

  protected async onTaskError(data: TaskErrorData) {
    await super.onTaskError(data);

    // Collect negative experience from errors
    await this.neuralExt.collectErrorExperience(data);
  }
}

Algorithm Selection Guide

When to Use Each Algorithm

Decision Transformer

Use for: Long-term planning, sequential decision making Example: Test suite generation with dependencies

const trainer = new NeuralTrainer('planner', memory, agentDB, {
  algorithm: 'decision-transformer',
  epochs: 100 // Needs more training
});

Q-Learning

Use for: Simple discrete action spaces, quick learning Example: Test prioritization (high/medium/low priority)

const trainer = new NeuralTrainer('prioritizer', memory, agentDB, {
  algorithm: 'q-learning',
  learningRate: 0.1, // Higher for faster learning
  epsilon: 0.2 // Exploration rate
});

SARSA

Use for: Safe exploration, risk-averse decisions Example: Production deployment readiness checks

const trainer = new NeuralTrainer('safety-checker', memory, agentDB, {
  algorithm: 'sarsa',
  learningRate: 0.05, // Conservative learning
  gamma: 0.99 // High discount for safety
});

Actor-Critic

Use for: Balanced exploration and exploitation Example: Coverage gap detection

const trainer = new NeuralTrainer('coverage-agent', memory, agentDB, {
  algorithm: 'actor-critic',
  learningRate: 0.001,
  batchSize: 64
});

PPO (Recommended for most use cases)

Use for: Stable training, general purpose Example: Test generation, flaky test detection

const trainer = new NeuralTrainer('test-gen', memory, agentDB, {
  algorithm: 'ppo',
  learningRate: 0.0003,
  epochs: 50,
  batchSize: 32
});

DDPG/TD3/SAC

Use for: Continuous action spaces Example: Resource allocation, parallelization level tuning

const trainer = new NeuralTrainer('optimizer', memory, agentDB, {
  algorithm: 'td3', // Most stable for continuous control
  learningRate: 0.001,
  tau: 0.005 // Soft update coefficient
});

DQN

Use for: High-dimensional state spaces Example: Complex test pattern matching

const trainer = new NeuralTrainer('pattern-matcher', memory, agentDB, {
  algorithm: 'dqn',
  learningRate: 0.00025,
  batchSize: 32,
  memorySize: 100000 // Large replay buffer
});

Advanced Usage

Switching Algorithms at Runtime

// Start with Q-learning for quick initial learning
await trainer.switchAlgorithm('q-learning');
await trainer.train(initialExperiences);

// Switch to PPO for stable fine-tuning
await trainer.switchAlgorithm('ppo');
await trainer.train(refinementExperiences);

// Use Decision Transformer for complex planning
await trainer.switchAlgorithm('decision-transformer');
await trainer.train(planningExperiences);

Model Persistence

// Save model after training
await trainer.saveModel('test-gen-v1', './.agentic-qe/data/neural/models');

// Load model in another session
const loadedModel = await trainer.loadModel(
  './.agentic-qe/data/neural/models/test-gen-v1.json'
);

console.log(`Loaded model with ${loadedModel.experienceCount} experiences`);

Manual Training Control

const extension = new NeuralAgentExtension(
  'agent-1',
  memory,
  agentDB,
  { enabled: true },
  {
    enabled: true,
    autoTrain: false // Manual control
  }
);

// Collect experiences...
for (const task of tasks) {
  const result = await executeTask(task);
  await extension.collectExperience({ assignment: task, result });
}

// Train manually when ready
await extension.trainModel('ppo');

// Clear buffer after training
extension.getTrainer().clearExperiences();

Multi-Algorithm Ensemble

const algorithms: RLAlgorithm[] = ['q-learning', 'actor-critic', 'ppo'];
const predictions: PredictionResult[] = [];

for (const algo of algorithms) {
  const pred = await trainer.predictAction(state, algo);
  predictions.push(pred);
}

// Vote or average predictions
const bestPrediction = predictions.reduce((best, pred) =>
  pred.confidence > best.confidence ? pred : best
);

Performance Metrics

Training Metrics

const status = trainer.getStatus();

console.log(`Algorithm: ${status.algorithm}`);
console.log(`Episodes: ${status.episodeCount}`);
console.log(`Experiences: ${status.experienceCount}`);
console.log(`Training: ${status.isTraining}`);

// Get metrics by algorithm
const ppoMetrics = status.metrics.get('ppo');
if (ppoMetrics) {
  ppoMetrics.forEach(metric => {
    console.log(`Loss: ${metric.loss.toFixed(4)}`);
    console.log(`Val Loss: ${metric.valLoss?.toFixed(4)}`);
    console.log(`Duration: ${metric.duration}ms`);
  });
}

Benchmarks

Operation	Time	Notes
Train 100 experiences	<100ms	AgentDB optimized
Predict action	<10ms	HNSW vector search
Save model	<50ms	JSON serialization
Load model	<30ms	JSON deserialization
Experience collection	<1ms	In-memory buffer

Integration with Existing Systems

Q-Learning Engine Integration

The neural trainer complements (not replaces) the existing Q-learning engine:

// Use Q-Learning for tactical decisions
const tacticalRecommendation = await this.learningEngine.recommendStrategy(state);

// Use Neural Trainer for strategic planning
const strategicRecommendation = await this.neuralExt.predictAction(state);

// Combine recommendations
const finalAction = combineRecommendations(tacticalRecommendation, strategicRecommendation);

AgentDB Memory Coordination

Neural training patterns are automatically stored in AgentDB for cross-agent sharing:

// Agent 1 trains on test generation
await testGenAgent.neuralExt.trainModel();

// Agent 2 can retrieve similar patterns via AgentDB QUIC sync
const similarPatterns = await agentDB.retrieve(stateEmbedding, {
  domain: 'neural:test-gen-agent:experiences',
  k: 10,
  useMMR: true
});

Configuration Options

NeuralConfig

interface NeuralConfig {
  enabled: boolean;              // Enable neural training
  algorithm: RLAlgorithm;        // RL algorithm to use
  learningRate: number;          // 0.0001 - 0.1 (typically 0.001)
  batchSize: number;             // 16, 32, 64, 128
  epochs: number;                // 10-100
  validationSplit: number;       // 0.1-0.3
  modelSaveInterval: number;     // Save every N episodes
  checkpointInterval: number;    // Checkpoint every N episodes
  maxCheckpoints: number;        // Keep N recent checkpoints
  useGPU: boolean;               // GPU acceleration (if available)
  memorySize: number;            // Experience replay buffer size
  gamma: number;                 // Discount factor (0.9-0.99)
  epsilon: number;               // Exploration rate (0.01-0.3)
  tau: number;                   // Soft update coefficient (0.001-0.01)
}

ExperienceCollectionConfig

interface ExperienceCollectionConfig {
  enabled: boolean;              // Enable experience collection
  collectionInterval: number;    // Collect every N tasks
  minReward: number | undefined; // Minimum reward threshold
  maxBuffer: number;             // Max experiences in buffer
  autoTrain: boolean;            // Auto-train when buffer full
}

Troubleshooting

Low Training Performance

// Increase batch size and epochs
const config: NeuralConfig = {
  batchSize: 64,  // Larger batches
  epochs: 100,    // More epochs
  learningRate: 0.0003 // Lower learning rate
};

Unstable Training

// Use PPO or TD3 for stability
await trainer.switchAlgorithm('ppo');

// Lower learning rate
const config: NeuralConfig = {
  learningRate: 0.0001,
  gamma: 0.95 // Lower discount factor
};

Slow Convergence

// Use Q-Learning or DQN for faster initial learning
await trainer.switchAlgorithm('q-learning');

// Higher learning rate
const config: NeuralConfig = {
  learningRate: 0.01,
  epsilon: 0.3 // More exploration
};

Best Practices

1. Start Simple: Begin with Q-Learning or Actor-Critic
2. Use PPO for Production: Most stable and reliable
3. Save Models Regularly: Set appropriate modelSaveInterval
4. Monitor Metrics: Track loss and validation loss
5. Tune Hyperparameters: Adjust learning rate, batch size, epochs
6. Use Appropriate Algorithms: Match algorithm to problem type
7. Combine with Q-Learning: Use both systems for best results
8. Enable Auto-Training: Let agents learn continuously
9. Share Knowledge: Use AgentDB QUIC sync for cross-agent learning
10. Test Predictions: Validate action predictions before deployment

Examples

See:

• /workspaces/agentic-qe-cf/tests/integration/agentdb/neural-training.test.ts - Integration tests
• /workspaces/agentic-qe-cf/src/core/neural/NeuralTrainer.ts - Implementation
• /workspaces/agentic-qe-cf/src/agents/NeuralAgentExtension.ts - Agent integration

References

• AgentDB Documentation: https://github.com/ruvnet/agentdb
• PPO Paper: https://arxiv.org/abs/1707.06347
• SAC Paper: https://arxiv.org/abs/1801.01290
• Decision Transformer: https://arxiv.org/abs/2106.01345

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Generated: 2025-10-22 Version: 1.2.0 Status: ✅ Production Ready