code: def collect_exp(env, rpm, agent):
      obs = env.reset()
      # collect data to fulfill replay memory
      for i in tqdm(range(MEMORY_SIZE)):
          context = rpm.recent_obs()
          context.append(obs)
          context = np.stack(context, axis=0)
          action = agent.sample(context)
  
          next_obs, reward, isOver, _ = env.step(action)
          rpm.append(Experience(obs, action, reward, isOver))
          obs = next_obs
  
file_path: PARL/benchmark/fluid/offline-Q-learning/parallel_run.py
imports: from parl.utils import check_version_for_fluid
  import argparse
  import gym
  import numpy
  import os
  import time
  from tqdm import tqdm
  import parl
  import paddle.fluid
  from parl.utils import get_gpu_count
  from parl.utils import summary, logger
  from dqn import DQN
  from atari_agent import AtariAgent
  from atari_model import AtariModel
  from replay_memory import ReplayMemory, Experience
  from utils import get_player
label_desc: Use image node_id 2 for calling a global function with example usage: collect_exp(env, rpm, agent) without return types