class_docstr: This class creates a continous-state maze problem given a map.
class_docstr_tok: ["This","class","creates","a","continous-state","maze","problem","given","a","map","."]
code: def _load_map(self):
      self.room_lengths = np.array([1.0, 1.0])
      self.initial_state = np.array([0.0, 0.0])
      self.goal_state = np.array([1.0, 1.0])
      self.goal_width = 0.01
      self.motion_noise = (
          0.05  # probability of no-motion (staying in same state)
      )
      self.motion_mean = 0.1  # mean of motion length
      self.motion_std = 0.1 * self.motion_mean  # std of motion length
      self.motion_max = 2.0 * self.motion_mean
      self.puddle_centers = []
      self.puddle_radii = []
      self._build_puddle(np.array([0.2, 0.4]), 0.1)
      self._build_puddle(np.array([0.5, 0.8]), 0.1)
      self._build_puddle(np.array([0.9, 0.1]), 0.1)
      self.num_puddles = len(self.puddle_centers)
      self.puddle_cost = 2.0
  
file_path: CNTK/Examples/ReinforcementLearning/deeprl/env/puddleworld.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 PuddleWorld obj's underlying member method code with example usage: obj._load_map() without return types