code: def normalize_q_value(self):
          '''
          Normalize q-value.
          
          Override.
          
          This method is called in each learning steps.
          
          For example:
              self.q_df.q_value = self.q_df.q_value / self.q_df.q_value.sum()
          '''
          if self.q_df is not None and self.q_df.shape[0]:
              # min-max normalization
              self.q_df.q_value = (self.q_df.q_value - self.q_df.q_value.min()) / (self.q_df.q_value.max() - self.q_df.q_value.min()f normalize_q_value(self):
          '''
          Normalize q-value.
          
          Override.
          
          This method is called in each learning steps.
          
          For example:
              self.q_df.q_value = self.q_df.q_value / self.q_df.q_value.sum()
          '''
          if self.q_df is not None and self.q_df.shape[0]:
              # min-max normalization
              self.q_df.q_value = (self.q_df.q_value - self.q_df.q_value.min()) / (self.q_df.q_value.max() - self.q_df.q_value.min())
docs: Normalize q-value.
          
          Override.
          
          This method is called in each learning steps.
          
          For example:
              self.q_df.q_value = self.q_df.q_value / self.q_df.q_value.sum()