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# ✅ 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!**
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