code: def run_evaluate_episodes(agent, env, render=False):
      eval_reward = []
      for i in range(5):
          obs = env.reset()
          episode_reward = 0
          while True:
              action = agent.predict(obs)  # 预测动作，只选最优动作
              obs, reward, done, _ = env.step(action)
              episode_reward += reward
              if render:
                  env.render()
              if done:
                  break
          eval_reward.append(episode_reward)
      return np.mean(eval_reward)
  
file_path: PARL/examples/tutorials/parl2_dygraph/lesson3/homework/dqn_mountaincar/train.py
imports: import gym
  import parl
  import paddle
  import os
  import gym
  import numpy
  import parl
  from parl.utils import logger
  from model import Model
  from parl.algorithms import DQN
  from agent import Agent
  from replay_memory import ReplayMemory
label_desc: Use image node_id 2 for calling a global function with example usage: run_evaluate_episodes(agent, env, render) and returns: np