teacher_agent_dev/SUMMARY.md

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

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