agentdb-reinforcement-learning-training

AgentDB Reinforcement Learning Training

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LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

• Location: .claude/library/catalog.json
• If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

• Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
• If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

• Location: D:\Projects\*
• If found: EXTRACT and adapt

Decision Matrix

Match	Action
Library >90%	REUSE directly
Library 70-90%	ADAPT minimally
Pattern exists	FOLLOW pattern
In project	EXTRACT
No match	BUILD (add to library after)

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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