text: import numpy as np
  import sys
  from contextlib import closing
  from io import StringIO
  from gym.envs.toy_text import discrete
  
  UP = 0
  RIGHT = 1
  DOWN = 2
  LEFT = 3
  
  
  class CliffWalkingEnv(discrete.DiscreteEnv):
      """
      This is a simple implementation of the Gridworld Cliff
      reinforcement learning task.
  
      Adapted from Example 6.6 (page 106) from Reinforcement Learning: An Introduction
      by Sutton and Barto:
      http://incompleteideas.net/book/bookdraft2018jan1.pdf
  
      With inspiration from:
      https://github.com/dennybritz/reinforcement-learning/blob/master/lib/envs/cliff_walking.py
  
      The board is a 4x12 matrix, with (using Numpy matrix indexing):
          [3, 0] as the start at bottom-left
          [3, 11] as the goal at bottom-right
          [3, 1..10] as the cliff at bottom-center
  
      Each time step incurs -1 reward, and stepping into the cliff incurs -100 reward
      and a reset to the start. An episode terminates when the agent reaches the goal.
      """
      metadata = {'render.modes': ['human', 'ansi']}
  
      def __init__(self):
          self.shape = (4, 12)
          self.start_state_index = np.ravel_multi_index((3, 0), self.shape)
  
          nS = np.prod(self.shape)
          nA = 4
  
          # Cliff Location
          self._cliff = np.zeros(self.shape, dtype=np.bool)
          self._cliff[3, 1:-1] = True
  
          # Calculate transition probabilities and rewards
          P = {}
          for s in range(nS):
              position = np.unravel_index(s, self.shape)
              P[s] = {a: [] for a in range(nA)}
              P[s][UP] = self._calculate_transition_prob(position, [-1, 0])
              P[s][RIGHT] = self._calculate_transition_prob(position, [0, 1])
              P[s][DOWN] = self._calculate_transition_prob(position, [1, 0])
              P[s][LEFT] = self._calculate_transition_prob(position, [0, -1])
  
          # Calculate initial state distribution
          # We always start in state (3, 0)
          isd = np.zeros(nS)
          isd[self.start_state_index] = 1.0
  
          super(CliffWalkingEnv, self).__init__(nS, nA, P, isd)
  
      def _limit_coordinates(self, coord):
          """
          Prevent the agent from falling out of the grid world
          :param coord:
          :return:
          """
          coord[0] = min(coord[0], self.shape[0] - 1)
          coord[0] = max(coord[0], 0)
          coord[1] = min(coord[1], self.shape[1] - 1)
          coord[1] = max(coord[1], 0)
          return coord
  
      def _calculate_transition_prob(self, current, delta):
          """
          Determine the outcome for an action. Transition Prob is always 1.0.
          :param current: Current position on the grid as (row, col)
          :param delta: Change in position for transition
          :return: (1.0, new_state, reward, done)
          """
          new_position = np.array(current) + np.array(delta)
          new_position = self._limit_coordinates(new_position).astype(int)
          new_state = np.ravel_multi_index(tuple(new_position), self.shape)
          if self._cliff[tuple(new_position)]:
              return [(1.0, self.start_state_index, -100, False)]
  
          terminal_state = (self.shape[0] - 1, self.shape[1] - 1)
          is_done = tuple(new_position) == terminal_state
          return [(1.0, new_state, -1, is_done)]
  
      def render(self, mode='human'):
          outfile = StringIO() if mode == 'ansi' else sys.stdout
  
          for s in range(self.nS):
              position = np.unravel_index(s, self.shape)
              if self.s == s:
                  output = " x "
              # Print terminal state
              elif position == (3, 11):
                  output = " T "
              elif self._cliff[position]:
                  output = " C "
              else:
                  output = " o "
  
              if position[1] == 0:
                  output = output.lstrip()
              if position[1] == self.shape[1] - 1:
                  output = output.rstrip()
                  output += '\n'
  
              outfile.write(output)
          outfile.write('\n')
  
          # No need to return anything for human
          if mode != 'human':
              with closing(outfile):
                  return outfile.getvalue()