text: import tensorflow as tf
  
  import os
  
  from clue_tf_env import ClueGameEnv
  from paths import Board
  
  from tf_agents.policies import policy_saver
  from tf_agents.environments import tf_py_environment
  
  tf.compat.v1.enable_v2_behavior()
  
  Board.calculate_room_distances()
  
  eval_py_env = ClueGameEnv(eval = True)
  eval_tf_env = tf_py_environment.TFPyEnvironment(eval_py_env)
  
  #policy_dir = os.path.join("models_no_delete", "reinforce-5", "policy")
  policy_dir = os.path.join("models_no_delete", "best_guess_policy")
  saved_policy = tf.compat.v2.saved_model.load(policy_dir)
  
  num_episodes = 500
  wins = 0
  
  for cnt in range(num_episodes):
      time_step = eval_tf_env.reset()
  
      while not time_step.is_last():
          action_step = saved_policy.action(time_step)
          time_step = eval_tf_env.step(action_step.action)
  
      if eval_py_env._clue.winner == eval_py_env._ai_player:
          wins += 1
  
      if cnt % 50 == 0:
          print("Game " + str(cnt) + " of " + str(num_episodes) +"; Win Ratio = " + str(wins / (cnt + 1)))
  
  print("Wins = " + str(wins))
  print("Games Played = " + str(num_episodes))
  print("Win ratio = " + str((wins / num_episodes) * 100) + "%")
  
  
  