class_docstr: Modified version of the CliffWalking environment from Farama-Foundation's
  Gymnasium with walls instead of a cliff.
  
  ### Description
  The board is a 4x12 matrix, with (using NumPy matrix indexing):
  - [3, 0] or obs==36 as the start at bottom-left
  - [3, 11] or obs==47 as the goal at bottom-right
  - [3, 1..10] or obs==37...46 as the cliff at bottom-center
  
  An episode terminates when the agent reaches the goal.
  
  ### Actions
  There are 4 discrete deterministic actions:
  - 0: move up
  - 1: move right
  - 2: move down
  - 3: move left
  You can also use the constants ACTION_UP, ACTION_RIGHT, ... defined above.
  
  ### Observations
  There are 3x12 + 2 possible states, not including the walls. If an action
  would move an agent into one of the walls, it simply stays in the same position.
  
  ### Reward
  Each time step incurs -1 reward, except reaching the goal which gives +10 reward.
code: def step(self, action):
      x = self.position // 12
      y = self.position % 12
      # UP
      if action == ACTION_UP:
          x = max(x - 1, 0)
      # RIGHT
      elif action == ACTION_RIGHT:
          if self.position != 36:
              y = min(y + 1, 11)
      # DOWN
      elif action == ACTION_DOWN:
          if self.position < 25 or self.position > 34:
              x = min(x + 1, 3)
      # LEFT
      elif action == ACTION_LEFT:
          if self.position != 47:
              y = max(y - 1, 0)
      else:
          raise ValueError(
              f"action {action} not in {self.action_space}"
          )
      self.position = x * 12 + y
      done = self.position == 47
      reward = -1 if not done else 10
      return self.position, reward, done, False, {}
  
imports: import gymnasium
  from gymnasium import spaces
label_desc: Use image node_id 3 for calling the CliffWalkingWallEnv obj's underlying member method code with example usage: obj.step(action) and returns: self,  reward,  done,  False,  dict