code: def _updatePropensities(self, lastState, lastAction, reward):
          
          phi = self.recency
  
          for action in range(self.module.numActions):
              carryOver = (1 - phi) * self.module.getValue(lastState, action)
              experience = self._experience(lastState, action, lastAction,reward)
  
              self.module.updateValue(lastState, action, carryOver + experience)
docs: Update the propensities for all actions. The propensity for last
          action chosen will be updated using the feedback value that resulted
          from performing the action.
  
          If j is the index of the last action chosen, r_j is the reward received
          for performing j, i is the current action being updated, q_i is the
          propensity for i, and phi is the recency parameter, then this update
          function can be expressed as::
  
                  q_i = (1-phi) * q_i + E(i, r_j)