agentdb-reinforcement-learning-training

skill_id	when-training-rl-agents-use-agentdb-learning
name	agentdb-reinforcement-learning-training
description	Train AI agents using AgentDB's 9 reinforcement learning algorithms including Q-Learning, DQN, PPO, and Actor-Critic. Build self-learning agents, implement RL training loops with experience replay, and deploy optimized models to production.
version	1.0.0
category	agentdb
subcategory	machine-learning
trigger_pattern	when-training-rl-agents
agents	["ml-developer","safla-neural","performance-benchmarker"]
complexity	advanced
estimated_duration	6-10 hours
prerequisites	["AgentDB basics","Reinforcement learning fundamentals","Neural network knowledge","Python/TypeScript proficiency"]
outputs	["Trained RL agents","Learning plugin modules","Performance benchmarks","Deployment pipeline"]
validation_criteria	["Training converges successfully","Reward curve shows improvement","Agent passes validation tasks","Benchmarks meet targets"]
evidence_based_techniques	["Self-consistency validation","Program-of-thought decomposition","Chain-of-verification","Multi-agent consensus"]
metadata	{"author":"claude-flow","created":"2025-10-30T00:00:00.000Z","updated":"2025-10-30T00:00:00.000Z","tags":["agentdb","reinforcement-learning","neural-networks","ai-training","q-learning"]}

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

2. Initialize learning database

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

const learningDB = new AgentDB({
  name: 'rl-training-db',
  : , 
  : {
    : ,
    : ,
    : 
  }
});

 learningDB.();


 learningPlugin =  ({
  : learningDB,
  : [, , , ],
  : {
    : ,
    : ,
    : ,
    : ,
    : 
  }
});

 learningPlugin.();