agentdb-reinforcement-learning-training

Train AI learning plugins with AgentDB's 9 reinforcement learning algorithms for building self-learning agents and optimizing agent behavior through experience.

When to Use

• Train autonomous agents that learn from experience
• Implement Q-Learning, DQN, PPO, Actor-Critic, SAC, and more
• Build self-improving AI systems
• Deploy RL agents in production

Quick Start

npm install agentdb-learning @agentdb/rl-algorithms
npx ts-node quickstart-rl.ts

5-Phase Workflow

1. Initialize Learning Environment (1-2 hrs) - Setup AgentDB, define environment
2. Configure RL Algorithm (1-2 hrs) - Select algorithm, set hyperparameters
3. Train Agents (3-4 hrs) - Execute training loop, monitor progress
4. Validate Performance (1-2 hrs) - Benchmark, compare with baseline
5. Deploy Trained Agents (1-2 hrs) - Export model, create API, monitor

Available RL Algorithms

• Q-Learning, SARSA, DQN
• Actor-Critic, A2C, PPO
• TD3, SAC, Decision Transformer

Success Metrics

• Reward curve stabilizes and improves
• Success rate > 80%
• Mean reward exceeds baseline by 50%
• Inference time < 10ms per action

Agents Used

• ml-developer: Algorithm configuration and deployment
• safla-neural: Neural network training
• performance-benchmarker: Performance evaluation

Estimated Duration

6-10 hours for complete implementation

Additional Resources

• Full SKILL.md documentation
• Detailed process walkthrough
• AgentDB Learning Docs: https://agentdb.dev/docs/learning