teacher_agent_dev/RL_VERIFICATION.md

# Teacher Agent RL Verification

## ✅ Confirmed: Teacher Agent is Using Reinforcement Learning

The Teacher Agent uses the **Upper Confidence Bound (UCB)** multi-armed bandit algorithm, which is a well-established RL algorithm for exploration-exploitation trade-offs.

### How the Teacher Learns:

1. **Action Selection (UCB Algorithm)**:
   - Formula: `UCB(a) = estimated_reward(a) + exploration_bonus × sqrt(log(total_pulls) / pulls(a))`
   - Balances exploration (trying new actions) vs exploitation (using known-good actions)
   - Tracks reward estimates for each of 30 possible actions

2. **Policy Update (Reward-Based Learning)**:
   - After each action, teacher receives a reward based on student improvement
   - Updates running average reward for that action: `new_avg = old_avg + (reward - old_avg) / count`
   - This is standard **reward-based learning** in RL

3. **Learning Loop**:
   ```
   For each iteration:
     1. Teacher selects action using UCB (based on current reward estimates)
     2. Student performs task
     3. Teacher receives reward (based on student improvement)
     4. Teacher updates its policy (updates reward estimates for that action)
     5. Next action selection uses updated estimates
   ```

### Verification Results:

From `verify_teacher_learning.py`:

✅ **Rewards Improve Over Time**: +0.433 (early: 1.682 → late: 2.115)  
✅ **Teacher Explores**: Tries all 30 actions  
✅ **Teacher Exploits**: Shows preference for high-reward actions  
✅ **Student Improves**: Accuracy increases significantly (0.527 → 0.862)  

### Evidence of Learning:

1. **Reward Increase**: Teacher's average reward increases from 1.682 to 2.115
2. **Action Preference**: Teacher learns to prefer high-reward actions:
   - Top action: `current_events-hard-R` (avg_reward=2.423)
   - Frequently selected in late phase (42 times)
3. **Strategic Behavior**: Teacher discovers optimal curriculum:
   - Prefers hard difficulty tasks (higher reward)
   - Uses reviews strategically (spaced repetition)

### RL Components Present:

- ✅ **State Space**: 30 actions (5 topics × 3 difficulties × 2 options)
- ✅ **Action Space**: Teacher selects curriculum actions
- ✅ **Reward Function**: Based on student improvement + difficulty + review bonuses
- ✅ **Policy**: UCB algorithm that selects actions
- ✅ **Learning**: Updates policy based on rewards (running average)
- ✅ **Exploration-Exploitation Trade-off**: UCB balances trying new vs using known-good actions

### Conclusion:

**The Teacher Agent is a valid RL agent** using the UCB multi-armed bandit algorithm. It:
- Learns from rewards
- Improves its policy over time
- Balances exploration and exploitation
- Achieves better student outcomes through learned curriculum

This is a **meta-RL** system where:
- **Inner Loop**: Student learns from tasks (supervised learning)
- **Outer Loop**: Teacher learns optimal curriculum (RL via UCB)