class_docstr: This class creates a maze problem given a map.
class_docstr_tok: ["This","class","creates","a","maze","problem","given","a","map","."]
code: def _build_wall(self, start, end):
      x_min = np.maximum(0, np.minimum(start[0], end[0]))
      x_max = np.minimum(
          self.room_lengths[0] - 1, np.maximum(start[0], end[0])
      )
      y_min = np.maximum(0, np.minimum(start[1], end[1]))
      y_max = np.minimum(
          self.room_lengths[1] - 1, np.maximum(start[1], end[1])
      )
      for x in range(x_min, x_max + 1):
          for y in range(y_min, y_max + 1):
              if not (
                  [x, y] in self.goal_states
                  or [x, y] in self.initial_states
              ):
                  self.wall_states.append([x, y])
  
file_path: CNTK/Examples/ReinforcementLearning/deeprl/env/maze2d.py
imports: import gym
  import numpy
  from gym import spaces
  from gym.utils import seeding
label_desc: Use image node_id 8 for calling the Maze2D obj's underlying member method code with example usage: obj._build_wall(start, end) without return types