Self-Optimization

Overview

Implements the SONA (Self-Optimizing Neural Architecture) adaptation cycle with sub-millisecond weight updates, EWC++ to prevent catastrophic forgetting, and a ReasoningBank for trajectory-based learning.

When to Use

• After task completion to extract and persist learnings
• Improving routing and agent selection over time
• Adapting to new project patterns without forgetting old ones
• Building cross-session intelligence

SONA Cycle

1. Extract Patterns - Mine execution data for recurring patterns
2. RETRIEVE - Search ReasoningBank for matching trajectories
3. JUDGE - Evaluate trajectory applicability in current context
4. DISTILL - Compress and store new entries
5. Adapt - Update weights with EWC++ regularization

Anti-Forgetting (EWC++)

• Elastic Weight Consolidation prevents overwriting previously learned patterns
• Fisher information matrix tracks parameter importance
• Configurable regularization penalty for new adaptations

RL Algorithms

Q-Learning, SARSA, PPO, DQN, A2C, TD3, SAC, DDPG, Rainbow

Agents Used

• agents/optimizer/ - Performance tuning
• agents/adaptive-queen/ - Real-time adaptation

Tool Use

Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence