agentdb-reinforcement-learning-training

AgentDB Reinforcement Learning Training

Overview

Train AI learning plugins with AgentDB's 9 reinforcement learning algorithms including Decision Transformer, Q-Learning, SARSA, Actor-Critic, PPO, and more. Build self-learning agents, implement RL, and optimize agent behavior through experience.

When to Use This Skill

Use this skill when you need to:

• Train autonomous agents that learn from experience
• Implement reinforcement learning systems
• Optimize agent behavior through trial and error
• Build self-improving AI systems
• Deploy RL agents in production environments
• Benchmark and compare RL algorithms

Available RL Algorithms

1. Q-Learning - Value-based, off-policy
2. SARSA - Value-based, on-policy
3. Deep Q-Network (DQN) - Deep RL with experience replay
4. Actor-Critic - Policy gradient with value baseline
5. Proximal Policy Optimization (PPO) - Trust region policy optimization
6. Decision Transformer - Offline RL with transformers
7. Advantage Actor-Critic (A2C) - Synchronous advantage estimation
8. Twin Delayed DDPG (TD3) - Continuous control
9. Soft Actor-Critic (SAC) - Maximum entropy RL

SOP Framework: 5-Phase RL Training Deployment

Phase 1: Initialize Learning Environment (1-2 hours)

Objective: Setup AgentDB learning infrastructure with environment configuration

Agent: ml-developer

Steps:

1. Install AgentDB Learning Module

npm install agentdb-learning@latest
npm install @agentdb/rl-algorithms @agentdb/environments

1. Initialize learning database

import { AgentDB, LearningPlugin } from 'agentdb-learning';

const learningDB = new AgentDB({
  name: 'rl-training-db',
  dimensions: 512, // State embedding dimension
  learning: {
    enabled: true,
    persistExperience: true,
    replayBufferSize: 100000
  }
});

await learningDB.initialize();

// Create learning plugin
const learningPlugin = new LearningPlugin({
  database: learningDB,
  algorithms: ['q-learning', 'dqn', 'ppo', 'actor-critic'],
  config: {
    batchSize: 64,
    learningRate: 0.001,
    discountFactor: 0.99,
    explorationRate: 1.0,
    explorationDecay: 0.995
  }
});

await learningPlugin.initialize();

1. Define environment

import { Environment } from '@agentdb/environments';

const environment = new Environment({
  name: 'grid-world',
  stateSpace: {
    type: 'continuous',
    shape: [10, 10],
    bounds: [[0, 10], [0, 10]]
  },
  actionSpace: {
    type: 'discrete',
    actions: ['up', 'down', 'left', 'right']
  },
  rewardFunction: (state, action, nextState) => {
    // Distance to goal reward
    const goalDistance = Math.sqrt(
      Math.pow(nextState[0] - 9, 2) +
      Math.pow(nextState[1] - 9, 2)
    );
    return -goalDistance + (goalDistance === 0 ? 100 : 0);
  },
  terminalCondition: (state) => {
    return state[0] === 9 && state[1] === 9; // Reached goal
  }
});

await environment.initialize();

1. Setup monitoring

const monitor = learningPlugin.createMonitor({
  metrics: ['reward', 'loss', 'exploration-rate', 'episode-length'],
  logInterval: 100, // Log every 100 episodes
  saveCheckpoints: true,
  checkpointInterval: 1000
});

monitor.on('episode-complete', (episode) => {
  console.log('Episode:', episode.number, 'Reward:', episode.totalReward);
});

Memory Pattern:

await agentDB.memory.store('agentdb/learning/environment', {
  name: environment.name,
  stateSpace: environment.stateSpace,
  actionSpace: environment.actionSpace,
  initialized: Date.now()
});

Validation:

• Learning database initialized
• Environment configured and tested
• Monitor capturing metrics
• Configuration stored in memory

Phase 2: Configure RL Algorithm (1-2 hours)

Objective: Select and configure RL algorithm for the learning task

Agent: ml-developer

Steps:

1. Select algorithm

// Example: Deep Q-Network (DQN)
const dqnAgent = learningPlugin.createAgent({
  algorithm: 'dqn',
  config: {
    networkArchitecture: {
      layers: [
        { type: 'dense', units: 128, activation: 'relu' },
        { type: 'dense', units: 128, activation: 'relu' },
        { type: 'dense', units: environment.actionSpace.size, activation: 'linear' }
      ]
    },
    learningRate: 0.001,
    batchSize: 64,
    replayBuffer: {
      size: 100000,
      prioritized: true,
      alpha: 0.6,
      beta: 0.4
    },
    targetNetwork: {
      updateFrequency: 1000,
      tauSync: 0.001 // Soft update
    },
    exploration: {
      initial: 1.0,
      final: 0.01,
      decay: 0.995
    },
    training: {
      startAfter: 1000, // Start training after 1000 experiences
      updateFrequency: 4
    }
  }
});

await dqnAgent.initialize();

1. Configure hyperparameters

const hyperparameters = {
  // Learning parameters
  learningRate: 0.001,
  discountFactor: 0.99, // Gamma
  batchSize: 64,

  // Exploration
  epsilonStart: 1.0,
  epsilonEnd: 0.01,
  epsilonDecay: 0.995,

  // Experience replay
  replayBufferSize: 100000,
  minReplaySize: 1000,
  prioritizedReplay: true,

  // Training
  maxEpisodes: 10000,
  maxStepsPerEpisode: 1000,
  targetUpdateFrequency: 1000,

  // Evaluation
  evalFrequency: 100,
  evalEpisodes: 10
};

dqnAgent.setHyperparameters(hyperparameters);

1. Setup experience replay

import { PrioritizedReplayBuffer } from '@agentdb/rl-algorithms';

const replayBuffer = new PrioritizedReplayBuffer({
  capacity: 100000,
  alpha: 0.6, // Prioritization exponent
  beta: 0.4, // Importance sampling
  betaIncrement: 0.001,
  epsilon: 0.01 // Small constant for stability
});

dqnAgent.setReplayBuffer(replayBuffer);

1. Configure training loop

const trainingConfig = {
  episodes: 10000,
  stepsPerEpisode: 1000,
  warmupSteps: 1000,
  trainFrequency: 4,
  targetUpdateFrequency: 1000,
  saveFrequency: 1000,
  evalFrequency: 100,
  earlyStoppingPatience: 500,
  earlyStoppingThreshold: 0.01
};

dqnAgent.setTrainingConfig(trainingConfig);

Memory Pattern:

await agentDB.memory.store('agentdb/learning/algorithm-config', {
  algorithm: 'dqn',
  hyperparameters: hyperparameters,
  trainingConfig: trainingConfig,
  configured: Date.now()
});

Validation:

• Algorithm selected and configured
• Hyperparameters validated
• Replay buffer initialized
• Training config set

Phase 3: Train Agents (3-4 hours)

Objective: Execute training iterations and optimize agent behavior

Agent: safla-neural

Steps:

1. Start training loop

async function trainAgent() {
  console.log('Starting RL training...');

  const trainingStats = {
    episodes: [],
    totalReward: [],
    episodeLength: [],
    loss: [],
    explorationRate: []
  };

  for (let episode = 0; episode < trainingConfig.episodes; episode++) {
    let state = await environment.reset();
    let episodeReward = 0;
    let episodeLength = 0;
    let episodeLoss = 0;

    for (let step = 0; step < trainingConfig.stepsPerEpisode; step++) {
      // Select action
      const action = await dqnAgent.selectAction(state, {
        explore: true
      });

      // Execute action
      const { nextState, reward, done } = await environment.step(action);

      // Store experience
      await dqnAgent.storeExperience({
        state,
        action,
        reward,
        nextState,
        done
      });

      // Train if enough experiences
      if (dqnAgent.canTrain()) {
        const loss = await dqnAgent.train();
        episodeLoss += loss;
      }

      episodeReward += reward;
      episodeLength += 1;
      state = nextState;

      if (done) break;
    }

    // Update target network
    if (episode % trainingConfig.targetUpdateFrequency === 0) {
      await dqnAgent.updateTargetNetwork();
    }

    // Decay exploration
    dqnAgent.decayExploration();

    // Log progress
    trainingStats.episodes.push(episode);