agentdb-reinforcement-learning-traininglisted

# 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** ```bash npm install agentdb-learning@latest npm install @agen