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"""Main training loop for Teacher Agent system."""
import numpy as np
from typing import Dict, Tuple
from interfaces import Task
from mock_student import MockStudentAgent
from mock_task_generator import MockTaskGenerator
from teacher_agent import TeacherAgent, compute_reward
def train_teacher(num_iterations: int = 500, verbose: bool = True, seed: int = 42) -> Tuple[Dict, TeacherAgent, MockStudentAgent]:
"""
Train teacher agent with mock student.
Args:
num_iterations: Number of training iterations
verbose: Whether to print progress
seed: Random seed
Returns:
Tuple of (history dict, teacher agent, student agent)
"""
# Initialize components
generator = MockTaskGenerator(seed=seed)
teacher = TeacherAgent(exploration_bonus=2.0, task_generator=generator) # Pass generator for dynamic action space
student = MockStudentAgent(learning_rate=0.15, forgetting_rate=0.01, seed=seed) # Reduced forgetting rate
# Create evaluation set (held-out tasks for measuring student performance)
eval_tasks = []
for topic in generator.get_available_topics():
for _ in range(3): # 3 tasks per topic
eval_tasks.append(generator.generate_task(topic, 'medium'))
if verbose:
print("=" * 70)
print("TEACHER AGENT TRAINING")
print("=" * 70)
print(f"Iterations: {num_iterations}")
print(f"Evaluation tasks: {len(eval_tasks)}")
print(f"Action space: {teacher.num_actions} actions")
print("=" * 70)
# Track metrics
history = {
'iterations': [],
'student_accuracies': [],
'teacher_rewards': [],
'actions': [],
'topics': [],
'difficulties': [],
'is_reviews': []
}
for iteration in range(num_iterations):
# 1. Get student state
student_state = student.get_state()
# 2. Teacher selects action
action = teacher.select_action(student_state)
# 3. Generate task
# For reviews, use same topic but maybe different difficulty
if action.is_review:
# Review: use same topic, medium difficulty
task = generator.generate_task(action.topic, 'medium')
else:
# New material: use specified topic and difficulty
task = generator.generate_task(action.topic, action.difficulty)
# 4. Evaluate student BEFORE learning
accuracy_before = student.evaluate(eval_tasks)
# 5. Student learns from task
was_correct = student.learn(task)
# 6. Evaluate student AFTER learning
accuracy_after = student.evaluate(eval_tasks)
# 7. Compute reward for teacher
reward = compute_reward(
accuracy_before,
accuracy_after,
action.difficulty,
action.is_review
)
# 8. Update teacher's policy
teacher.update(action, reward)
# 9. Time passes (for forgetting)
student.advance_time(1.0)
# 10. Log metrics
history['iterations'].append(iteration)
history['student_accuracies'].append(accuracy_after)
history['teacher_rewards'].append(reward)
history['actions'].append(action)
history['topics'].append(action.topic)
history['difficulties'].append(action.difficulty)
history['is_reviews'].append(action.is_review)
# 11. Print progress
if verbose and (iteration % 50 == 0 or iteration == num_iterations - 1):
window = min(50, iteration + 1)
recent_rewards = history['teacher_rewards'][-window:]
avg_reward = np.mean(recent_rewards) if recent_rewards else 0.0
print(f"Iteration {iteration:3d} | "
f"Student Acc: {accuracy_after:.3f} | "
f"Avg Reward: {avg_reward:.3f} | "
f"Action: {action.topic[:3]}-{action.difficulty[:2]}-{'R' if action.is_review else 'N'}")
if verbose:
print("=" * 70)
print(f"Final accuracy: {history['student_accuracies'][-1]:.3f}")
print(f"Average reward: {np.mean(history['teacher_rewards']):.3f}")
print("=" * 70)
return history, teacher, student
def train_baseline_random(num_iterations: int = 500, seed: int = 42) -> Dict:
"""Train with random teacher (baseline)."""
import random
rng = random.Random(seed)
student = MockStudentAgent(learning_rate=0.15, forgetting_rate=0.05, seed=seed)
generator = MockTaskGenerator(seed=seed)
topics = generator.get_available_topics()
difficulties = generator.get_available_difficulties()
eval_tasks = [
generator.generate_task(topic, 'medium')
for topic in topics
for _ in range(3)
]
history = {
'iterations': [],
'student_accuracies': [],
'teacher_rewards': [],
'actions': [],
'topics': [],
'difficulties': [],
'is_reviews': []
}
for iteration in range(num_iterations):
# Random action
topic = rng.choice(topics)
difficulty = rng.choice(difficulties)
is_review = rng.random() < 0.3 # 30% chance of review
task = generator.generate_task(topic, 'medium' if is_review else difficulty)
accuracy_before = student.evaluate(eval_tasks)
student.learn(task)
accuracy_after = student.evaluate(eval_tasks)
reward = compute_reward(accuracy_before, accuracy_after, difficulty, is_review)
student.advance_time(1.0)
history['iterations'].append(iteration)
history['student_accuracies'].append(accuracy_after)
history['teacher_rewards'].append(reward)
history['topics'].append(topic)
history['difficulties'].append(difficulty)
history['is_reviews'].append(is_review)
return history
def train_baseline_fixed(num_iterations: int = 500, seed: int = 42) -> Dict:
"""Train with fixed curriculum (easy→medium→hard, sequential topics)."""
student = MockStudentAgent(learning_rate=0.15, forgetting_rate=0.05, seed=seed)
generator = MockTaskGenerator(seed=seed)
topics = generator.get_available_topics()
difficulties = ['easy', 'medium', 'hard']
eval_tasks = [
generator.generate_task(topic, 'medium')
for topic in topics
for _ in range(3)
]
history = {
'iterations': [],
'student_accuracies': [],
'teacher_rewards': [],
'actions': [],
'topics': [],
'difficulties': [],
'is_reviews': []
}
# Fixed curriculum: cycle through topics, increase difficulty over time
phase_length = num_iterations // (len(topics) * len(difficulties))
for iteration in range(num_iterations):
# Determine phase
phase = iteration // phase_length
topic_idx = (phase // len(difficulties)) % len(topics)
diff_idx = phase % len(difficulties)
topic = topics[topic_idx]
difficulty = difficulties[diff_idx]
task = generator.generate_task(topic, difficulty)
accuracy_before = student.evaluate(eval_tasks)
student.learn(task)
accuracy_after = student.evaluate(eval_tasks)
reward = compute_reward(accuracy_before, accuracy_after, difficulty, False)
student.advance_time(1.0)
history['iterations'].append(iteration)
history['student_accuracies'].append(accuracy_after)
history['teacher_rewards'].append(reward)
history['topics'].append(topic)
history['difficulties'].append(difficulty)
history['is_reviews'].append(False)
return history
if __name__ == "__main__":
# Train teacher agent
print("\n" + "=" * 70)
print("TRAINING TEACHER AGENT")
print("=" * 70)
history, teacher, student = train_teacher(num_iterations=500, verbose=True)
# Print statistics
stats = teacher.get_statistics()
print(f"\nTeacher Statistics:")
print(f" Total actions tried: {stats['total_pulls']}")
print(f" Unique actions: {np.sum(stats['action_counts'] > 0)}/{stats['total_pulls']}")
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