RL-PowerTracking/examples/evaluate_model.py

"""
模型评估示例
"""
import os
import numpy as np
import gym
from stable_baselines3 import PPO

# 添加项目根目录到Python路径
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from src.rl_env.cartesian_env import CartesianSpaceEnv

def evaluate_model(model_path, num_episodes=5):
    """
    评估训练好的模型
    
    参数:
        model_path: 模型文件路径
        num_episodes: 要运行的测试回合数
    """
    # 创建环境
    env = CartesianSpaceEnv()
    
    # 加载模型
    model = PPO.load(model_path)
    
    print(f"开始评估，共 {num_episodes} 个episode")
    
    for episode in range(num_episodes):
        obs, _ = env.reset()
        done = False
        total_reward = 0.0
        steps = 0
        
        print(f"\nEpisode {episode + 1}/{num_episodes}")
        print(f"目标位置: {env._target_pos}")
        
        while not done:
            action, _states = model.predict(obs, deterministic=True)
            obs, reward, done, truncated, info = env.step(action)
            total_reward += reward
            steps += 1
            
            # 显示中间步骤信息
            if steps % 10 == 0:
                distance = np.linalg.norm(env._target_pos - env._current_pos)
                print(f"Step {steps}: 距离={distance:.4f}, 累计奖励={total_reward:.4f}")
            
            # 渲染最后一步
            if done or truncated:
                env.render()
                distance = np.linalg.norm(env._target_pos - env._current_pos)
                print(f"\n最终结果:")
                print(f"总步数: {steps}")
                print(f"最终距离: {distance:.4f}")
                print(f"总奖励: {total_reward:.4f}")
                
    env.close()

if __name__ == "__main__":
    # 使用最佳模型进行评估
    MODEL_PATH = "models/best_model/best_model"
    
    # 运行评估
    evaluate_model(MODEL_PATH)