Gomoku(Five in a Row, Omok) (2/5) - 속도 최적화 1차 (minimax 속도 개선)

minimax 속도를 개선하기 위해 다음과 같은 변경사항을 적용하였다.

1. 빈 자리를 검색할 때, 이미 돌이 놓여진 위치 주변에 있는 빈 자리만 반환.

2. AI가 돌을 놓았을 때, 승리 조건이 만족되는 위치라면 즉시 돌을 놓는다.

3. 플레이어가 돌을 놓았을 때, 승리 조건이 만족되는 위치라면 AI가 그 위치에 돌을 즉시 놓는다.

 

1번 빈 자리 검색 시 아래 그림과 같이 돌이 놓여진 주변 빈자리만 검색하여 반환되도록 한다.

반환된 빈 자리는 불필요한 트리 탐색을 줄여 속도가 개선된다.

빈 자리 검색시 반환되는 위치

 

#
# FIAR
# Created by netcanis on 2023/10/07.
#
# minimax
# alpha beta pruning
# 속도 개선 

import pygame
import random
import numpy as np
import sys


BOARD_SIZE = 9  # (7,9,13,15,19)
CELL_SIZE = 40
MARGIN = 20
CLICK_RADIUS = CELL_SIZE // 2

BLACK = (0, 0, 0)
WHITE = (255, 255, 255)
RED = (255, 0, 0)
GRAY = (100, 100, 100)

NUM_ITEMS = (BOARD_SIZE * BOARD_SIZE)
PLAYER = 1
AI = -1

DIFFICULTY_LEVEL = 3


class FIAR:
    def __init__(self):
        self.init_game()
    
    def init_game(self):
        pygame.init()
        pygame.display.set_caption("FIAR")
        w = (BOARD_SIZE-1) * CELL_SIZE + 2 * MARGIN
        h = (BOARD_SIZE-1) * CELL_SIZE + 2 * MARGIN
        self.screen = pygame.display.set_mode((w, h))

        self.font = pygame.font.Font(None, CELL_SIZE-4)

    def reset_game(self):
        self.board = np.zeros((BOARD_SIZE, BOARD_SIZE), dtype=int)
        self.sequences = np.zeros((BOARD_SIZE, BOARD_SIZE), dtype=int)
        self.sequence = 0
        self.game_over = False
        self.turn_player = PLAYER
        
        self.draw_board(self.screen)
        pygame.display.flip()
         
    def find_empty_cells(self):
        empty_cells = []
        for row in range(BOARD_SIZE):
            for col in range(BOARD_SIZE):
                if self.board[row][col] == 0:
                    empty_cells.append((row, col))
        random.shuffle(empty_cells)
        return empty_cells
    
    # 수가 놓여진 자리의 주변 빈 자리만 반환
    def find_adjacent_empty_cells(self):
        empty_cells = []
        for row, col in self.find_empty_cells():
            for dr in [-1, 0, 1]:
                for dc in [-1, 0, 1]:
                    new_row, new_col = row + dr, col + dc
                    if 0 <= new_row < BOARD_SIZE and 0 <= new_col < BOARD_SIZE and self.board[new_row][new_col] != 0:
                        empty_cells.append((row, col))
                        break
        return empty_cells
    
    def check_winner(self, board, row, col):
        def check_direction(dx, dy):
            count = 1
            r, c = row + dx, col + dy
            while 0 <= r < len(board) and 0 <= c < len(board[0]) and board[r][c] == board[row][col]:
                count += 1
                r += dx
                c += dy
            return count

        directions = [(0, 1), (1, 0), (1, 1), (-1, 1)]
        for dx, dy in directions:
            count1 = check_direction(dx, dy)
            count2 = check_direction(-dx, -dy)
            total_count = count1 + count2 - 1
            if total_count == 5:
                return True

        return False
    
    def is_board_full(self, board):
        return all(cell != 0 for row in board for cell in row)

    def random_move(self, player):
        if self.game_over:
            return -1, -1
        row, col = random.choice(self.find_adjacent_empty_cells())
        self.make_move(row, col, player)
        return row, col

    def minimax_move(self, player):
        if self.game_over:
            return -1, -1
        row, col = self.find_best_move(player)
        self.make_move(row, col, player)
        return row, col

    def make_move(self, row, col, player):
        if self.board[row][col] == 0:
            self.board[row][col] = player
            
            self.sequence += 1
            self.sequences[row][col] = self.sequence
            
            print(f"[{self.sequence}] <{player}> {row},{col}")
            
            if self.check_winner(self.board, row, col):
                self.game_over = True
            elif self.is_board_full(self.board):
                self.game_over = True
                self.turn_player = 0
            else:
                self.turn_player *= -1

    def minimax(self, depth, is_maximizing, alpha, beta, row2, col2):
        if self.is_board_full(self.board):
            return 0
        
        if is_maximizing:
            if self.check_winner(self.board, row2, col2):
                return -(NUM_ITEMS - depth + 1)
        else:
            if self.check_winner(self.board, row2, col2):
                return (NUM_ITEMS - depth + 1)

        if depth >= DIFFICULTY_LEVEL:
            return 0
        
        if is_maximizing:# AI
            best_score = -float('inf')
            for row, col in self.find_adjacent_empty_cells():
                self.board[row][col] = AI
                score = self.minimax(depth + 1, False, alpha, beta, row, col)
                self.board[row][col] = 0
                best_score = max(best_score, score)
                alpha = max(alpha, best_score)
                if beta <= alpha:
                    break
            return best_score
        else:
            best_score = float('inf')
            for row, col in self.find_adjacent_empty_cells():
                self.board[row][col] = PLAYER
                score = self.minimax(depth + 1, True, alpha, beta, row, col)
                self.board[row][col] = 0
                best_score = min(best_score, score)
                beta = min(beta, best_score)
                if beta <= alpha:
                    break
            return best_score
    
    # 아래 우선순위에 따라 즉시 처리할 자리는 minimax 알고리즘 없이 즉시 수를 놓아 속도 개선
    # 1.만약 AI가 놓으면 게임을 이기는 자리라면 즉시 수를 놓는다. 
    # 2.만약 Player가 놓으면 게임을 이기는 자리라면 AI는 그 곳에 수는 놓는다.
    def make_optimal_move(self, player):
        # 0. The first one is random.
        if self.sequence == 0:
            row = BOARD_SIZE // 2 + random.randint(-2, 2)
            col = BOARD_SIZE // 2 + random.randint(-2, 2)
            return (row, col)
        
        empty_cells = self.find_adjacent_empty_cells()

        # 1. ai turn: 5
        for row, col in empty_cells: 
            self.board[row][col] = player
            if self.check_winner(self.board, row, col):
                self.board[row][col] = 0
                return (row, col)
            self.board[row][col] = 0
            
        # 2. player turn: 5
        for row, col in empty_cells: 
            self.board[row][col] = -player
            if self.check_winner(self.board, row, col):
                self.board[row][col] = 0
                return (row, col)
            self.board[row][col] = 0
        
        return None
        
    def find_best_move(self, player):
        
        print(f"[{self.sequence+1}] <{player}> Calculating...")
        
        optimal_move = self.make_optimal_move(player)
        if optimal_move != None:
            return optimal_move
        
        alpha = -float('inf')
        beta = float('inf')

        best_move = None
        best_score = -float('inf') if player == AI else float('inf')

        empty_cells = self.find_adjacent_empty_cells()
        for index, (row, col) in enumerate(empty_cells):
            self.board[row][col] = player
            is_maximizing = False if player == AI else True
            score = self.minimax(0, is_maximizing, alpha, beta, row, col)
            self.board[row][col] = 0
            
            if (player == AI and score > best_score) or (player == PLAYER and score < best_score):
                best_score = score
                best_move = (row, col)
            
            percentage = (index / len(empty_cells)) * 100
            print(f"    [{percentage:.1f}%] <{player}> {row},{col} -> {score}")
            
        return best_move
    
    def show_message(self, message, is_exit=False):
        popup = True
        while popup:
            for event in pygame.event.get():
                if event.type == pygame.QUIT:
                    pygame.quit()
                    sys.exit()

                if event.type == pygame.KEYDOWN:
                    if is_exit:
                        if event.key == pygame.K_y:
                            self.reset_game()
                            popup = False
                            return
                        elif event.key == pygame.K_n:
                            pygame.quit() 
                            sys.exit()
                    else:
                        popup = False
                        break
            
            text_lines = message.split('\n')
            for i, line in enumerate(text_lines):
                text = self.font.render(line, True, (128, 0, 128))
                text_rect = text.get_rect(topleft=(20, 20 + i * 20))
                self.screen.blit(text, text_rect)
            
            pygame.display.flip()
    
    def draw_board(self, screen):
        screen.fill(GRAY)
        for row in range(BOARD_SIZE):# draw horizontal lines
            pygame.draw.line(screen, BLACK, 
                            (0 * CELL_SIZE + MARGIN, row * CELL_SIZE + MARGIN), 
                            ((BOARD_SIZE-1) * CELL_SIZE + MARGIN, row * CELL_SIZE + MARGIN),
                            1)
            for col in range(BOARD_SIZE):# draw vertical lines
                if row == 0:
                    pygame.draw.line(screen, BLACK, 
                                    (col * CELL_SIZE + MARGIN, 0 * CELL_SIZE + MARGIN), 
                                    (col * CELL_SIZE + MARGIN, (BOARD_SIZE-1) * CELL_SIZE + MARGIN),
                                    1)

                x = col * CELL_SIZE + MARGIN
                y = row * CELL_SIZE + MARGIN

                if self.board[row][col] == PLAYER:
                    pygame.draw.circle(screen, BLACK, (x, y), CLICK_RADIUS)
                elif self.board[row][col] == AI:
                    pygame.draw.circle(screen, WHITE, (x, y), CLICK_RADIUS)
                
                seq_no = self.sequences[row][col] 
                if seq_no != 0:
                    if seq_no == self.sequence: 
                        pygame.draw.circle(screen, RED, (x, y), CLICK_RADIUS+1, 1)
                    
                    color = WHITE if self.board[row][col] == PLAYER else BLACK
                    text = self.font.render(f"{seq_no}", True, color)
                    text_rect = text.get_rect(center=(x, y))
                    screen.blit(text, text_rect)

    def handle_events(self):
        for event in pygame.event.get():
            if event.type == pygame.QUIT:
                self.game_over = True
            elif event.type == pygame.MOUSEBUTTONDOWN and not self.game_over and self.turn_player == PLAYER:
                x, y = pygame.mouse.get_pos()
                row = int(round((y - MARGIN) / CELL_SIZE))
                col = int(round((x - MARGIN) / CELL_SIZE))
                if 0 <= row < BOARD_SIZE and 0 <= col < BOARD_SIZE and self.board[row][col] == 0:
                    self.make_move(row, col, self.turn_player)
                    self.draw_board(self.screen)
                    pygame.display.flip()
                    
                    if self.turn_player == AI:
                        best_move = self.find_best_move(self.turn_player)
                        if best_move is not None:
                            row, col = best_move
                            self.make_move(row, col, self.turn_player)
                        
                        self.draw_board(self.screen)
                        pygame.display.flip()
                            
                    if self.game_over == True:
                        if self.turn_player == 0:
                            self.show_message("Game draw!")
                        else:
                            self.show_message(f"Game Over!\n{'Player' if self.turn_player == PLAYER else 'AI'} wins!")

    
if __name__ == "__main__":
    game = FIAR()
    game.reset_game()
    
    while True:
        while not game.game_over:
            game.handle_events()
        
        game.show_message("Play Again? (y/n)", is_exit=True)

 

속도는 다소 빨라지긴 했다.

3x3을 만들면 이미 진 것을 예감한 것인지 AI는 그 때부터 엉뚱한 수를 놓는다.  

마치 포기한 것처럼...

 

2023.10.27 - [AI,ML, Algorithm] - Gomoku(Five in a Row, Omok) (1/5) - 기본 구현 (minimax, alpha-beta pruning)

2023.10.27 - [AI,ML, Algorithm] - Gomoku(Five in a Row, Omok) (2/5) - 속도 최적화 1차 (minimax 속도 개선)

2023.10.28 - [AI,ML, Algorithm] - Gomoku(Five in a Row, Omok) (3/5) - 속도 최적화 2차 (RANDOM모드 추가)

2023.10.29 - [AI,ML, Algorithm] - Gomoku(Five in a Row, Omok) (4/5) - 훈련 데이터 생성 및 학습

2023.10.29 - [AI,ML, Algorithm] - Gomoku(Five in a Row, Omok) (5/5) - 머신러닝으로 게임 구현

2023.11.03 - [AI,ML, Algorithm] - Gomoku(Five in a Row, Omok) (5/5) - 3x3 체크 추가