(self, action_int)
Args:
            action_int: integer representation of discretized action

        Returns:
            list: (action, raise_size) in "continuous" PokerEnv format
def _get_env_adjusted_action_formulation(self, action_int):
        """

        Args:
            action_int: integer representation of discretized action

        Returns:
            list: (action, raise_size) in "continuous" PokerEnv format

        """
        if action_int == 0:
            return [0, -1]
        if action_int == 1:
            return [1, -1]
        elif action_int > 1:
            selected = self.get_fraction_of_pot_raise(fraction=self.bet_sizes_list_as_frac_of_pot[action_int - 2],
                                                      player_that_bets=self.current_player)

            if self.uniform_action_interpolation and not self.IS_EVALUATING:
                # _________________________________________ The maximal amount _________________________________________
                if action_int == self.N_ACTIONS - 1:  # if highest bet in repertoire
                    if self.IS_POT_LIMIT_GAME:  # if PL game: max is pot
                        max_amnt = self.get_fraction_of_pot_raise(fraction=1.0, player_that_bets=self.current_player)
                    elif self.IS_FIXED_LIMIT_GAME:
                        raise EnvironmentError("Should not get here with a limit game!")
                    else:  # if NL game: max is allin
                        max_amnt = self.current_player.stack + self.current_player.current_bet

                else:  # else, max is the mean of the selected and the next-bigger bet size
                    bigger = self.get_fraction_of_pot_raise(fraction=self.bet_sizes_list_as_frac_of_pot[action_int - 1],
                                                            player_that_bets=self.current_player)
                    max_amnt = int(float(selected + bigger) / 2)

                # _________________________________________ The minimal amount _________________________________________
                if action_int == 2:  # if lowest bet in repertoire, min is minbet
                    min_amnt = self._get_current_total_min_raise()

                else:  # else, min is the mean of the selected and the next-smaller bet size
                    smaller = self.get_fraction_of_pot_raise(
                        fraction=self.bet_sizes_list_as_frac_of_pot[action_int - 3],
                        player_that_bets=self.current_player)
                    min_amnt = int(float(selected + smaller) / 2)

                if min_amnt >= max_amnt:  # can happen. The sampling would always be the same -> save time.
                    return [2, min_amnt]
                return [2, np.random.randint(low=min_amnt, high=max_amnt)]

            else:
                return [2, selected]
        else:
            raise ValueError(action_int)
def _get_env_adjusted_action_formulation ( self , action_int ) : if action_int == 0 : return [ 0 , - 1 ] if action_int == 1 : return [ 1 , - 1 ] elif action_int > 1 : selected = self . get_fraction_of_pot_raise ( fraction = self . bet_sizes_list_as_frac_of_pot [ action_int - 2 ] , player_that_bets = self . current_player ) if self . uniform_action_interpolation and not self . IS_EVALUATING : # _________________________________________ The maximal amount _________________________________________ if action_int == self . N_ACTIONS - 1 : # if highest bet in repertoire if self . IS_POT_LIMIT_GAME : # if PL game: max is pot max_amnt = self . get_fraction_of_pot_raise ( fraction = 1.0 , player_that_bets = self . current_player ) elif self . IS_FIXED_LIMIT_GAME : raise EnvironmentError ( "Should not get here with a limit game!" ) else : # if NL game: max is allin max_amnt = self . current_player . stack + self . current_player . current_bet else : # else, max is the mean of the selected and the next-bigger bet size bigger = self . get_fraction_of_pot_raise ( fraction = self . bet_sizes_list_as_frac_of_pot [ action_int - 1 ] , player_that_bets = self . current_player ) max_amnt = int ( float ( selected + bigger ) / 2 ) # _________________________________________ The minimal amount _________________________________________ if action_int == 2 : # if lowest bet in repertoire, min is minbet min_amnt = self . _get_current_total_min_raise ( ) else : # else, min is the mean of the selected and the next-smaller bet size smaller = self . get_fraction_of_pot_raise ( fraction = self . bet_sizes_list_as_frac_of_pot [ action_int - 3 ] , player_that_bets = self . current_player ) min_amnt = int ( float ( selected + smaller ) / 2 ) if min_amnt >= max_amnt : # can happen. The sampling would always be the same -> save time. return [ 2 , min_amnt ] return [ 2 , np . random . randint ( low = min_amnt , high = max_amnt ) ] else : return [ 2 , selected ] else : raise ValueError ( action_int )