text: import gym
  import gym_sokoban
  from algorithms.sarsa import run_sarsa
  from algorithms.montecarlo import run_montecarlo
  from algorithms.qlearning import run_qlearning
  from sokoban_utils.policy import run_policy
  from sokoban_utils.utils import *
  from sokoban_utils.global_configs import GlobalConfigs
  import numpy as np
  
  env = gym.make('Boxoban-Train-v1')
   
  config = Config()
  config.total_episodes = 100
  
  print("[!] Max Epsilon Test")
  #Test montecarlo, sarsa and qlearning max epsilon (exploration probability at start) 
  sarsa_max_epsilon = open("logs/sarsa_max_epsilon.txt", "a+")
  montecarlo_max_epsilon = open("logs/montecarlo_max_epsilon.txt", "a+")
  qlearning_max_epsilon = open("logs/qlearning_max_epsilon.txt", "a+")
  
  max_epsilon_config = copy_config(config)
  
  # Defining list of values for the max epsilon
  max_epsilon_list =  [1.0]
  
  for max_epsilon in max_epsilon_list:
      max_epsilon_config.epsilon = max_epsilon
      max_epsilon_config.max_epsilon = max_epsilon
      _, logfile = run_sarsa(env, log=True, initial_config=max_epsilon_config)
      sarsa_max_epsilon.write(logfile + "\n")
      
      _, logfile = run_montecarlo(env, log=True, initial_config=max_epsilon_config)
      montecarlo_max_epsilon.write(logfile + "\n")
      
      _, logfile = run_qlearning(env, log=True, initial_config=max_epsilon_config)
      qlearning_max_epsilon.write(logfile + "\n")
  
  sarsa_max_epsilon.close()
  montecarlo_max_epsilon.close()
  qlearning_max_epsilon.close()
  
  env.close()
  