Approach #14: Reinforcement Learning from Corrections - Learn from Mistakes

[ikennaokpala]

RL from Corrections: Agent Learns from Past Mistakes

Each correction improves future performance. Build pattern database.

Learning Cycle

1. Agent implements (attempt 1)
2. Review finds issues
3. Agent fixes (attempt 2)
4. System records what changed
5. Next time: Agent applies learned pattern proactively

Corrections Database

CREATE TABLE corrections (
  context TEXT,  -- 'Implementing API call'
  mistake TEXT,  -- 'No error handling'
  fix TEXT,      -- 'Added try-catch'
  pattern TEXT,  -- 'Always add error handling to async calls'
  confidence FLOAT
);

Next Feature

Agent sees 'async API call' → Recalls pattern → Applies try-catch proactively

Strengths

✅ Continuous improvement
✅ Personalized (learns from actual mistakes)
✅ Proactive (prevents issues before they occur)

Rating: ⭐⭐⭐⭐⭐ (5/5) Essential for long-term quality

Agent: QA-14-RL-Corrections

Related: #4