import random


class TabularF(dict):
    '''
    Tabular representation for any of the two types of value functions:

    1. state value function (V-Functions).
       e.g.
       vf = TabularF()
       vf[state] = 1

    2. state-action value functions (Q-functions)
       e.g.
       qf = TabularF()
       qf[(state, action)] = 1

    '''

    def __getitem__(self, key):
        if key not in self:
            self[key] = random.random() - 0.5
        return super(TabularF, self).__getitem__(key)

import random

_MEAN = 0.0
_STD = 0.3

class TabularF(dict):
    '''
    Tabular representation for any of the two types of value functions:

    1. state value function (V-Functions).
       e.g.
       vf = TabularF()
       vf[state] = 1

    2. state-action value functions (Q-functions)
       e.g.
       qf = TabularF()
       qf[(state, action)] = 1

    '''

    def __getitem__(self, key):
        if key not in self:
            self[key] = random.gauss(_MEAN, _STD)
        return super(TabularF, self).__getitem__(key)

Initialize values using a gaussian distribution with mean = 0 and std = 0.3
Initialize values using a gaussian distribution with mean = 0 and std = 0.3

  import random
  
+ _MEAN = 0.0
+ _STD = 0.3
  
  class TabularF(dict):
      '''
      Tabular representation for any of the two types of value functions:
  
      1. state value function (V-Functions).
         e.g.
         vf = TabularF()
         vf[state] = 1
  
      2. state-action value functions (Q-functions)
         e.g.
         qf = TabularF()
         qf[(state, action)] = 1
  
      '''
  
      def __getitem__(self, key):
          if key not in self:
-             self[key] = random.random() - 0.5
+             self[key] = random.gauss(_MEAN, _STD)
          return super(TabularF, self).__getitem__(key)
  
Initialize values using a gaussian distribution with mean = 0 and std = 0.3
## Code Before:
import random


class TabularF(dict):
    '''
    Tabular representation for any of the two types of value functions:

    1. state value function (V-Functions).
       e.g.
       vf = TabularF()
       vf[state] = 1

    2. state-action value functions (Q-functions)
       e.g.
       qf = TabularF()
       qf[(state, action)] = 1

    '''

    def __getitem__(self, key):
        if key not in self:
            self[key] = random.random() - 0.5
        return super(TabularF, self).__getitem__(key)

## Instruction:
Initialize values using a gaussian distribution with mean = 0 and std = 0.3
## Code After:
import random

_MEAN = 0.0
_STD = 0.3

class TabularF(dict):
    '''
    Tabular representation for any of the two types of value functions:

    1. state value function (V-Functions).
       e.g.
       vf = TabularF()
       vf[state] = 1

    2. state-action value functions (Q-functions)
       e.g.
       qf = TabularF()
       qf[(state, action)] = 1

    '''

    def __getitem__(self, key):
        if key not in self:
            self[key] = random.gauss(_MEAN, _STD)
        return super(TabularF, self).__getitem__(key)
