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 _compute_reward(self, state):
      reward = -1
      for i in range(self.num_puddles):
          delta = state - self.puddle_centers[i]
          dist = np.dot(delta, delta)
          if dist <= self.puddle_radii[i]:
              reward -= self.puddle_cost
      return reward
  
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 6 for calling the PuddleWorld obj's underlying member method code with example usage: obj._compute_reward(state) and returns: reward