File size: 2,220 Bytes
9cb3002 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import matplotlib.pyplot as plt
plt.switch_backend('Agg') # Fix for Windows MemoryError/Display issues
import pandas as pd
import numpy as np
import os
def plot_training_results(reward_history, loss_history, output_dir="plots"):
"""
Generate professional, readable plots for the training run.
"""
os.makedirs(output_dir, exist_ok=True)
plt.style.use('ggplot') # Clean, modern look
# 1. Reward Curve
plt.figure(figsize=(10, 6))
plt.plot(reward_history, label='Agent Reward', color='#3498db', linewidth=2)
plt.xlabel('Training Steps / Episodes')
plt.ylabel('Normalized Reward [0, 1]')
plt.title('Agent Performance Over Time (GRPO)')
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend()
plt.savefig(os.path.join(output_dir, "reward_curve.png"), dpi=300)
plt.close()
# 2. Loss Curve
plt.figure(figsize=(10, 6))
plt.plot(loss_history, label='Policy Loss', color='#e74c3c', linewidth=2)
plt.xlabel('Training Steps')
plt.ylabel('Loss Value')
plt.title('Convergence: Policy Loss Optimization')
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend()
plt.savefig(os.path.join(output_dir, "loss_curve.png"), dpi=300)
plt.close()
print(f"Plots saved to {output_dir}")
def plot_baseline_comparison(trained_grades, random_grades, output_dir="plots"):
"""
Compare the trained agent vs a random baseline.
"""
os.makedirs(output_dir, exist_ok=True)
plt.style.use('ggplot')
plt.figure(figsize=(10, 6))
plt.hist(random_grades, bins=20, alpha=0.5, label='Random Baseline', color='#95a5a6')
plt.hist(trained_grades, bins=20, alpha=0.7, label='Trained Agent', color='#2ecc71')
plt.axvline(np.mean(random_grades), color='#7f8c8d', linestyle='dashed', linewidth=1)
plt.axvline(np.mean(trained_grades), color='#27ae60', linestyle='dashed', linewidth=2)
plt.xlabel('Performance Grade [0, 1]')
plt.ylabel('Frequency (Episodes)')
plt.title('Performance Distribution: Baseline vs. Trained')
plt.legend()
plt.savefig(os.path.join(output_dir, "baseline_comparison.png"), dpi=300)
plt.close()
print(f"Comparison plot saved to {output_dir}")
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