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 _load_map(self):
      self.room_lengths = np.array([25, 25])
      self.initial_states = [[0, 0]]
      self.goal_states = [[24, 24]]
      self.wall_states = []
      self._build_wall([2, 0], [2, 15])
      self._build_wall([5, 10], [5, 20])
      self._build_wall([5, 12], [13, 12])
      self._build_wall([15, 5], [15, 24])
      self._build_wall([10, 5], [22, 5])
      self.num_states = self.room_lengths[0] * self.room_lengths[1]
      self.motion_noise = 0.05
  
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 5 for calling the Maze2D obj's underlying member method code with example usage: obj._load_map() without return types