code: def printPolicy(policy):
      
      p = np.array(policy).reshape(POPULATION_CLASSES, FIRE_CLASSES)
      range_F = range(FIRE_CLASSES)
      print("    " + " ".join("%2d" % f for f in range_F))
      print("    " + "---" * FIRE_CLASSES)
      for x in range(POPULATION_CLASSES):
          print(" %2d|" % x + " ".join("%2d" % p[x, f] for f in range_F))
docs: Print out a policy vector as a table to console
      
      Let ``S`` = number of states.
      
      The output is a table that has the population class as rows, and the years
      since a fire as the columns. The items in the table are the optimal action
      for that population class and years since fire combination.
      
      Parameters
      ----------
      p : array
          ``p`` is a numpy array of length ``S``.