Spaces:
Paused
Paused
File size: 3,339 Bytes
a52f96d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
# Teacher Agent System - Final Status Report
## ✅ VERIFICATION COMPLETE
### All Files Reviewed
**Status**: All files are relevant and necessary. No files to purge.
**File Inventory**:
1. ✅ `interfaces.py` - Core data structures 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 baseline comparisons
6. ✅ `test_teacher.py` - Unit tests (7/7 passing ✅)
7. ✅ `visualize.py` - Plotting utilities
8. ✅ `verify_teacher_learning.py` - RL verification script
9. ✅ `requirements.txt` - Python dependencies
10. ✅ `README.md` - Documentation
11. ✅ `RL_VERIFICATION.md` - RL proof document
12. ✅ `SUMMARY.md` - Quick reference
### ✅ Teacher Agent IS Using RL
**Algorithm**: Upper Confidence Bound (UCB) Multi-Armed Bandit
**Evidence of RL Learning**:
1. ✅ **Reward-Based Policy Updates**: Teacher updates action rewards based on feedback
2. ✅ **Exploration-Exploitation**: UCB balances trying new actions vs using known-good ones
3. ✅ **Policy Improvement**: Rewards increase from 1.682 → 2.115 (+0.433)
4. ✅ **Action Learning**: Teacher learns which actions are better (prefers high-reward actions)
### Verification Results
**From `verify_teacher_learning.py`**:
```
✅ Check 1: Teacher rewards improve over time (+0.433)
✅ Check 2: Teacher explores actions (30/30)
✅ Check 3: Teacher shows preference (top action selected 42 times)
✅ Check 4: Student improves significantly (0.527 → 0.862)
Total: 4/4 checks passed
✅ TEACHER AGENT IS LEARNING AND IMPROVING!
```
**From `test_teacher.py`**:
```
✅ All 7 tests pass:
- Task generator works
- Student learns
- Student forgets
- Teacher explores
- Teacher exploits
- Action encoding works
- Initial accuracy correct
```
### How Teacher Learns (RL Process)
1. **Select Action**: Uses UCB to choose action based on current reward estimates
2. **Execute**: Student performs task
3. **Receive Reward**: Based on student improvement + difficulty + review bonuses
4. **Update Policy**: Running average update: `new_avg = old_avg + (reward - old_avg) / count`
5. **Repeat**: Next selection uses updated estimates (learns from experience)
This is **standard RL**: Learning from rewards to improve policy.
### Key Metrics
- **Reward Improvement**: +0.433 (proves learning)
- **Top Action**: `current_events-hard-R` (avg_reward=2.423)
- **Student Improvement**: 0.527 → 0.862 accuracy (+0.335)
- **All Actions Explored**: 30/30
### System Status
**✅ READY FOR USE**
All components working:
- ✅ Teacher agent learns and improves
- ✅ Student learns and forgets realistically
- ✅ Task generator creates valid tasks
- ✅ Training loop functions correctly
- ✅ All tests pass
- ✅ Visualization tools work
### Next Steps
The system is complete and verified. When teammates finish real components:
1. Replace `mock_student.py` with real student agent
2. Replace `mock_task_generator.py` with real task generator
3. Keep `teacher_agent.py` (your RL algorithm)
4. All interfaces remain compatible
---
**Last Verified**: All checks passed ✅
**RL Status**: Confirmed learning and improving ✅
|