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# Teacher Agent System - Summary
## ✅ System Status: WORKING AND LEARNING
### Files Overview
All files in `teacher_agent_dev/` are **relevant and necessary**:
1. **interfaces.py** - Core data structures (Task, StudentState, TeacherAction) and ABC interfaces
2. **mock_student.py** - Student agent with learning + forgetting
3. **mock_task_generator.py** - Task generator (5 topics × 3 difficulties)
4. **teacher_agent.py** - ⭐ MAIN: UCB bandit RL algorithm
5. **train_teacher.py** - Training loop with baselines
6. **test_teacher.py** - Unit tests (all passing)
7. **visualize.py** - Plotting utilities
8. **verify_teacher_learning.py** - RL verification script
9. **requirements.txt** - Dependencies
10. **README.md** - Documentation
11. **RL_VERIFICATION.md** - RL proof document
### ✅ Teacher Agent is Using RL
**Algorithm**: Upper Confidence Bound (UCB) Multi-Armed Bandit
**How it learns**:
1. Selects action using UCB: `UCB(a) = estimated_reward(a) + exploration_bonus × sqrt(log(total_pulls) / pulls(a))`
2. Receives reward based on student improvement
3. Updates policy: Running average reward for each action
4. Next selection uses updated estimates (exploits good actions)
**Verification Results** (from `verify_teacher_learning.py`):
- ✅ Rewards improve: 1.682 → 2.115 (+0.433)
- ✅ Explores all 30 actions
- ✅ Exploits high-reward actions (prefers `current_events-hard-R`)
- ✅ Student improves: 0.527 → 0.862 accuracy
### Key Features
**Teacher Agent**:
- Uses UCB bandit (classic RL algorithm)
- 30 actions: 5 topics × 3 difficulties × 2 options
- Learns from rewards (policy updates)
- Balances exploration/exploitation
**Student Agent**:
- Learns with practice (learning_rate)
- Forgets over time (Ebbinghaus curve)
- Per-topic skill tracking
**Reward Function**:
- Base: student improvement
- Bonus: harder tasks (+2.0), successful reviews (+1.0)
- Penalty: wasted reviews (-0.5)
### Note on Student State
The teacher currently uses a **non-contextual** bandit (doesn't use `student_state` parameter). This is still valid RL (UCB for multi-armed bandit), but could be enhanced to be **contextual** by using student state in decisions.
### Quick Start
```bash
cd teacher_agent_dev
# Run tests
python test_teacher.py
# Train teacher
python train_teacher.py
# Verify learning
python verify_teacher_learning.py
```
### All Checks Passed ✅
- ✅ Teacher learns and improves (rewards increase)
- ✅ Teacher explores actions
- ✅ Teacher exploits good actions
- ✅ Student improves significantly
- ✅ All tests pass
- ✅ System is self-contained and functional
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