text: import sys
  sys.path.insert(0, '../../')
  
  from libs_common.RLStatsCompute import *
  
  import matplotlib.pyplot as plt
  
  result_path = "./results/"
  
  files = []
  files.append("./models/ddpg_baseline/run_0/result/result.log")
  files.append("./models/ddpg_baseline/run_1/result/result.log")
  files.append("./models/ddpg_baseline/run_2/result/result.log")
  files.append("./models/ddpg_baseline/run_3/result/result.log")
  files.append("./models/ddpg_baseline/run_4/result/result.log")
  files.append("./models/ddpg_baseline/run_5/result/result.log")
  files.append("./models/ddpg_baseline/run_6/result/result.log")
  files.append("./models/ddpg_baseline/run_7/result/result.log")
  rl_stats_compute_ddpg = RLStatsCompute(files, result_path + "ddpg_baseline.log")
  
  files = []
  files.append("./models/ddpg_curiosity/run_0/result/result.log")
  files.append("./models/ddpg_curiosity/run_1/result/result.log")
  files.append("./models/ddpg_curiosity/run_2/result/result.log")
  files.append("./models/ddpg_curiosity/run_3/result/result.log")
  files.append("./models/ddpg_curiosity/run_4/result/result.log")
  files.append("./models/ddpg_curiosity/run_5/result/result.log")
  files.append("./models/ddpg_curiosity/run_6/result/result.log")
  files.append("./models/ddpg_curiosity/run_7/result/result.log")
  rl_stats_compute_curiosity = RLStatsCompute(files, result_path + "ddpg_curiosity.log") 
  
  
  plt.cla()
  plt.ylabel("score")
  plt.xlabel("episode")
  plt.grid(color='black', linestyle='-', linewidth=0.1)
  
  plt.plot(rl_stats_compute_ddpg.games_mean, rl_stats_compute_ddpg.episode_mean, label="ddpg baseline", color='blue')
  plt.fill_between(rl_stats_compute_ddpg.games_mean, rl_stats_compute_ddpg.episode_lower, rl_stats_compute_ddpg.episode_upper, color='blue', alpha=0.2)
  
  
  plt.plot(rl_stats_compute_curiosity.games_mean, rl_stats_compute_curiosity.episode_mean, label="ddpg curiosity", color='red')
  plt.fill_between(rl_stats_compute_curiosity.games_mean, rl_stats_compute_curiosity.episode_lower, rl_stats_compute_curiosity.episode_upper, color='red', alpha=0.2)
  
  
  plt.legend(loc='lower right', borderaxespad=0.)
  plt.savefig(result_path + "score_per_episode.png", dpi = 300)
  
   
  
  
  plt.cla()
  plt.ylabel("score")
  plt.xlabel("iteration")
  plt.grid(color='black', linestyle='-', linewidth=0.1)
  
  plt.plot(rl_stats_compute_ddpg.iterations, rl_stats_compute_ddpg.episode_mean, label="ddpg baseline", color='blue')
  plt.fill_between(rl_stats_compute_ddpg.iterations, rl_stats_compute_ddpg.episode_lower, rl_stats_compute_ddpg.episode_upper, color='blue', alpha=0.2)
  
  plt.plot(rl_stats_compute_curiosity.iterations, rl_stats_compute_curiosity.episode_mean, label="ddpg curiosity", color='red')
  plt.fill_between(rl_stats_compute_curiosity.iterations, rl_stats_compute_curiosity.episode_lower, rl_stats_compute_curiosity.episode_upper, color='red', alpha=0.2)
  
  plt.legend(loc='lower right', borderaxespad=0.)
  plt.savefig(result_path + "score_per_iteration.png", dpi = 300)
  
  
  '''
  
  plt.cla()
  plt.ylabel("value")
  plt.xlabel("iteration")
  plt.grid(color='black', linestyle='-', linewidth=0.1)
  
  plt.plot(rl_stats_compute_curiosity.iterations, rl_stats_compute_curiosity.entropy_mean, label="entropy", color='orange')
  plt.fill_between(rl_stats_compute_curiosity.iterations, rl_stats_compute_curiosity.entropy_lower, rl_stats_compute_curiosity.entropy_upper, color='orange', alpha=0.2)
  
  
  plt.plot(rl_stats_compute_curiosity.iterations, rl_stats_compute_curiosity.curiosity_mean, label="curiosity", color='green')
  plt.fill_between(rl_stats_compute_curiosity.iterations, rl_stats_compute_curiosity.curiosity_lower, rl_stats_compute_curiosity.curiosity_upper, color='green', alpha=0.2)
  
  plt.legend(loc='lower right', borderaxespad=0.)
  plt.savefig(result_path + "internal_motivation.png", dpi = 300)
  
  
  
  plt.cla()
  plt.ylabel("value")
  plt.xlabel("iteration")
  plt.grid(color='black', linestyle='-', linewidth=0.1)
  
  plt.plot(rl_stats_compute_curiosity.iterations, rl_stats_compute_curiosity.forward_loss_mean, label="forward model loss", color='navy')
  plt.fill_between(rl_stats_compute_curiosity.iterations, rl_stats_compute_curiosity.forward_loss_lower, rl_stats_compute_curiosity.forward_loss_upper, color='navy', alpha=0.2)
  
  plt.legend(loc='lower right', borderaxespad=0.)
  plt.savefig(result_path + "forward_model_loss.png", dpi = 300)
  '''