agentdb-learning-plugins

预期结果:

• Successfully created Q-Learning agent plugin named 'grid-navigator'
• Algorithm: Q-Learning (Off-Policy, Value-Based Learning)
• Configuration Applied:
• - Learning rate: 0.001
• - Discount factor (gamma): 0.99
• - Exploration rate (epsilon): 0.1 with 0.995 decay
• Next Steps for Training:
• 1. Define states as numerical arrays (example: position coordinates)
• 2. Store experiences with state, action, reward, next_state, and done flag
• 3. Call training method with epoch count and batch size
• Plugin is ready to receive training experiences

预期结果:

• Training Completed Successfully
• Final Training Loss: 0.023
• Validation Loss: 0.028
• Training Duration: 1,523 milliseconds
• Total Epochs: 100
• Batch Size: 64
• Model shows good convergence with low overfitting (validation loss close to training loss)

预期结果:

• Available AgentDB Learning Algorithm Templates:
• 1. Decision Transformer - Offline RL via sequence modeling (recommended for beginners)
• 2. Q-Learning - Value-based off-policy learning for discrete actions
• 3. SARSA - On-policy value learning for safe exploration
• 4. Actor-Critic - Policy gradient with value baseline for continuous control
• 5. Active Learning - Query-based learning for label efficiency
• 6. Adversarial Training - Robustness enhancement against perturbations
• 7. Curriculum Learning - Progressive difficulty training
• 8. Federated Learning - Distributed privacy-preserving training
• 9. Multi-Task Learning - Transfer learning across related tasks