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

```bash
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:

```bash
pip install torch transformers
```

The student will automatically detect and use DistilBERT if available.

## Status

🎉 **All components complete and ready for use!**