code: def evaluate(env, agent):
      ep_reward = 0
      obs = env.reset()
      while True:
          action = agent.predict(obs)
          obs, reward, done, _ = env.step(action)
          ep_reward += reward
          if done:
              break
      return ep_reward
  
file_path: PARL/examples/others/deepes.py
imports: import gym
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(env, agent) and returns: ep_reward