Cleanup

    # Did X Win

    if self.check_winner(TicTacToeEnvironment.X):

      print("Winner")

      self._terminated = True

      return TicTacToeEnvironment.WIN_REWARD

  def check_winner(self, player):

    for i in range(3):

      row = np.sum(self._state[0][i][:])

      row = np.sum(self._state[0][i][:], axis=0)

      if np.all(row == player * 3):

        return True

      col = np.sum(self._state[0][:][i])

      col = np.sum(self._state[0][:][i], axis=0)

      if np.all(col == player * 3):

        return True

    diag1 = self._state[0][0][0] + self._state[0][1][1] + self._state[0][2][2]

    if np.all(diag1 == player * 3):

        return True

    diag2 = self._state[0][0][2] + self._state[0][1][1] + self._state[0][2][0]

    if np.all(diag2 == player * 3):

        return True

    return False

  def game_over(self):

def main():

  env = TicTacToeEnvironment()

  policy = TicTacToePolicy()

  start = time.time()

  end = time.time()

  timeout = 60 * 60  # One Hour

  while end - start < timeout:

  a3c = dc.rl.A3C(

        env, policy, entropy_weight=0, value_weight=0.0001, model_dir="/home/leswing/tictactoe")

      env, policy, entropy_weight=0, value_weight=0.0001)

  a3c.optimizer = dc.models.tensorgraph.TFWrapper(

      tf.train.AdamOptimizer, learning_rate=0.01)

    try:

      a3c.restore()

    except:

      print("Restore Failed")

      pass

    a3c.fit(100000)

  a3c.fit(10000)

  env.reset()

  while not env._terminated:

    print(env.display())

    print(action)

    print(env.step(action))

  print(env.display())

    end = time.time()

if __name__ == "__main__":