teacher_agent_dev/COMPARISON_README.md

Strategy Comparison: Teacher vs Baselines

Overview

This module compares three training strategies for the student agent:

1. Random Strategy: Student receives random questions from task generator until they can confidently pass difficult questions
2. Progressive Strategy: Student receives questions in progressive difficulty order (Easy → Medium → Hard) within each family sequentially
3. Teacher Strategy: RL teacher agent learns optimal curriculum using UCB bandit algorithm

Goal

Demonstrate that the Teacher-trained student performs best - achieving highest accuracy on difficult questions.

Running the Comparison

cd teacher_agent_dev
python compare_strategies.py

This will:

• Train all three strategies for 500 iterations
• Track accuracy on general questions and difficult questions
• Generate comparison plots showing all three strategies
• Print summary statistics

Output

Plot: comparison_all_strategies.png

The plot contains three subplots:

1. General Accuracy Over Time: Shows how student accuracy improves on medium-difficulty questions
2. Difficult Question Accuracy: KEY METRIC - Shows accuracy on hard questions (most important for demonstrating teacher superiority)
3. Learning Efficiency: Bar chart showing iterations to reach 75% target vs final performance

Key Metrics Tracked

• General Accuracy: Student performance on medium-difficulty questions from all topics
• Difficult Accuracy: Student performance on hard-difficulty questions (target metric)
• Iterations to Target: How many iterations until student reaches 75% accuracy on difficult questions
• Final Accuracy: Final performance after 500 iterations

Expected Results

The Teacher strategy should show:

• ✅ Highest final accuracy on difficult questions
• ✅ Efficient learning (good balance of speed and performance)
• ✅ Better curriculum (smarter topic/difficulty selection)

Example Output

STRATEGY COMPARISON SUMMARY
======================================================================
Random          | ✅ Reached       | Iterations:   51 | Final Acc: 0.760
Progressive     | ✅ Reached       | Iterations:  310 | Final Acc: 0.520
Teacher         | ✅ Reached       | Iterations:   55 | Final Acc: 0.880
======================================================================

Teacher wins with highest final accuracy!

Strategy Details

Random Strategy

• Completely random selection of topics and difficulties
• No curriculum structure
• Baseline for comparison
• May reach target quickly due to luck, but doesn't optimize learning

Progressive Strategy

• Rigid curriculum: Easy → Medium → Hard for each topic sequentially
• No adaptation to student needs
• Slow to reach difficult questions
• Doesn't account for forgetting or optimal pacing

Teacher Strategy

• RL-based curriculum learning
• Uses UCB bandit to balance exploration/exploitation
• Adapts based on student improvement (reward signal)
• Optimizes for efficient learning
• Can strategically review topics to prevent forgetting

Visualization Features

• Color coding: Teacher in green (highlighted as best), Random in red, Progressive in teal
• Line styles: Teacher with solid thick line, baselines with dashed/dotted
• Annotations: Final accuracy values labeled on plots
• Target line: 75% accuracy threshold marked on difficult question plot
• Summary statistics: Table showing which strategies reached target and when

Customization

You can modify parameters in compare_strategies.py:

num_iterations = 500  # Number of training iterations
target_accuracy = 0.75  # Target accuracy on difficult questions
seed = 42  # Random seed for reproducibility

Files

• compare_strategies.py - Main comparison script
• comparison_all_strategies.png - Generated comparison plot
• train_teacher.py - Teacher training logic
• mock_student.py - Student agent implementation
• mock_task_generator.py - Task generator

Notes

• All strategies use the same student parameters for fair comparison
• Evaluation uses held-out test sets
• Teacher strategy learns from rewards based on student improvement
• Results may vary slightly due to randomness, but teacher should consistently outperform baselines