student_agent_dev/STUDENT_AGENT_COMPLETE.md

✅ Student Agent System - Complete!

Summary

All components have been successfully created! The student agent system is ready for development and testing.

Files Created

✅ interfaces.py - Shared interfaces (matches teacher/task generator teams) ✅ memory_decay.py - Ebbinghaus forgetting curve model ✅ student_agent.py - DistilBERT-based student with online learning ✅ student_metrics.py - Comprehensive metrics tracking ✅ mock_teacher.py - Dummy teacher for independent testing ✅ mock_task_generator.py - Dummy task generator for independent testing ✅ test_student.py - Unit tests for all components ✅ visualize_student.py - Beautiful visualizations (6 plots) ✅ train_student.py - Main training script with full integration ✅ requirements.txt - All dependencies ✅ README.md - Complete documentation

Quick Start

cd student_agent_dev

# Install dependencies
pip install -r requirements.txt

# Run tests
python test_student.py

# Train student
python train_student.py

# Check visualizations
ls student_visualizations/

Key Features Implemented

1. DistilBERT Integration

   • Online learning (1 task at a time)
   • Multiple choice format support
   • Gradient accumulation for stability
   • Graceful fallback if transformers not available

2. Memory Decay (Ebbinghaus)

   • Realistic forgetting curves
   • Per-topic retention tracking
   • Configurable retention constant

3. Comprehensive Metrics

   • Overall accuracy tracking
   • Per-topic learning curves
   • Retention analysis
   • Sample efficiency metrics

4. Beautiful Visualizations

   • Learning curve with milestones
   • Per-topic curves
   • Retention analysis
   • Difficulty progression
   • Topic distribution
   • Sample efficiency

Integration Ready

The student agent uses the shared interfaces.py, so it will integrate seamlessly with:

• Real Teacher Agent (replace MockTeacherAgent)
• Real Task Generator (replace MockTaskGenerator)

Next Steps

1. Install dependencies if not already installed
2. Run tests to verify everything works
3. Train student to see learning in action
4. Review visualizations to analyze performance
5. Tune hyperparameters (learning_rate, retention_constant)
6. Integrate with real teacher/task generator when ready

Note on DistilBERT

The code includes graceful fallback if DistilBERT is not available (uses dummy model for testing). For full functionality:

pip install torch transformers

The student will automatically detect and use DistilBERT if available.

Status

🎉 All components complete and ready for use!