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2026-04-04 15:51:08 +08:00
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+# ==============================================================================
+# Author: Hongru Liu
+# Affiliation: School of Power and Energy, Northwestern Polytechnical University
+# Version: 1.0
+# Contact: hongruliu@mail.nwpu.edu.cn
+# ==============================================================================
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+
+class Logger(object):
+    def __init__(self):
+        """
+        初始化日志记录器，用于暂存训练过程中的各项指标
+        """
+        self.episode_rewards = []
+        
+    def record(self, reward):
+        """
+        记录每个 Episode 的总奖励
+        """
+        self.episode_rewards.append(reward)
+        
+    def plot_learning_curve(self, save_dir="."):
+        """
+        绘制并保存学习曲线 (Learning Curve)
+        """
+        # 确保保存目录存在
+        os.makedirs(save_dir, exist_ok=True)
+        
+        # 创建画布，严格设置白色背景
+        fig, ax = plt.subplots(figsize=(10, 6), facecolor='white')
+        ax.set_facecolor('white')
+        
+        # 绘制奖励曲线，使用加粗线条以满足论文发表的视觉要求
+        ax.plot(self.episode_rewards, linewidth=2.5, color='#1f77b4', label='Episode Reward')
+        
+        # 计算并绘制 10 个 Episode 的滑动平均线，让趋势更清晰
+        if len(self.episode_rewards) >= 10:
+            moving_avg = np.convolve(self.episode_rewards, np.ones(10)/10, mode='valid')
+            ax.plot(range(9, len(self.episode_rewards)), moving_avg, 
+                    linewidth=2.5, color='#ff7f0e', label='10-Episode Moving Average')
+
+        # 设置全英文的坐标轴标签和图例，调整字体大小
+        ax.set_xlabel('Episodes', fontsize=14, fontweight='bold')
+        ax.set_ylabel('Total Reward', fontsize=14, fontweight='bold')
+        ax.set_title('Training Learning Curve (Pendulum-v1)', fontsize=16, fontweight='bold')
+        
+        # 设置刻度字体大小
+        ax.tick_params(axis='both', which='major', labelsize=12)
+        
+        # 增加网格线并设置图例
+        ax.grid(True, linestyle='--', alpha=0.7)
+        ax.legend(fontsize=12, loc='lower right')
+        
+        # 紧凑布局并保存
+        plt.tight_layout()
+        save_path = os.path.join(save_dir, "learning_curve.png")
+        plt.savefig(save_path, dpi=300, facecolor=fig.get_facecolor(), edgecolor='none')
+        plt.close()
+        print(f"[*] Learning curve saved to: {save_path}")