text: import os
  import pandas as pd
  
  from rllab import config
  
  def process_result(exp_prefix, exp_name):
      # Open the default rllab path for storing results
      result_path = os.path.join(config.LOG_DIR, "s3", exp_prefix, exp_name, 'progress.csv')
      print("Processing result from",result_path) 
      
      # This example uses pandas to easily read in results and create a simple smoothed learning curve
      df = pd.read_csv(result_path)
      curve = df['AverageReturn'].rolling(window=max(1,int(0.05*df.shape[0])), min_periods=1, center=True).mean().values.flatten()
      max_ix = curve.argmax()
      max_score = curve.max()
      
      # The result dict can contain arbitrary values, but ALWAYS needs to have a "loss" entry.
      return dict(
          max_score=max_score,
          max_iter=max_ix,
          scores=curve, # returning the curve allows you to plot best, worst etc curve later
          loss=-max_score
          )