Tron.py

Author: Shikhar9425

AI-driven AI Tron/Tron.py

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+import pygame
+import sys
+
+# Define the game window size
+WINDOW_WIDTH = 800
+WINDOW_HEIGHT = 600
+
+# Define colors
+WHITE = (255, 255, 255)
+BLACK = (0, 0, 0)
+RED = (255, 0, 0)
+GREEN = (0, 255, 0)
+BLUE = (0, 0, 255)
+
+# Define tile size and number of tiles
+TILE_SIZE = 20
+NUM_TILES_X = WINDOW_WIDTH // TILE_SIZE
+NUM_TILES_Y = WINDOW_HEIGHT // TILE_SIZE
+
+# Define directions
+UP = (0, -1)
+DOWN = (0, 1)
+LEFT = (-1, 0)
+RIGHT = (1, 0)
+
+
+class TronGame:
+    def __init__(self):
+        pygame.init()
+        self.screen = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))
+        pygame.display.set_caption("AI-driven AI Tron")
+        self.clock = pygame.time.Clock()
+        self.running = True
+        self.board = [[BLACK for _ in range(NUM_TILES_X)] for _ in range(NUM_TILES_Y)]
+        self.player1_pos = (NUM_TILES_X // 4, NUM_TILES_Y // 2)
+        self.player2_pos = (NUM_TILES_X * 3 // 4, NUM_TILES_Y // 2)
+        self.player1_direction = RIGHT
+        self.player2_direction = LEFT
+
+    def draw_board(self):
+        for y in range(NUM_TILES_Y):
+            for x in range(NUM_TILES_X):
+                pygame.draw.rect(self.screen, self.board[y][x], (x * TILE_SIZE, y * TILE_SIZE, TILE_SIZE, TILE_SIZE))
+
+    def move_player(self, player_pos, player_direction, player_color):
+        x, y = player_pos
+        dx, dy = player_direction
+        new_x, new_y = x + dx, y + dy
+
+        if not (0 <= new_x < NUM_TILES_X) or not (0 <= new_y < NUM_TILES_Y):
+            return False
+
+        if self.board[new_y][new_x] == BLACK:
+            self.board[new_y][new_x] = player_color
+            return (new_x, new_y)
+        return False
+
+    def update(self):
+        new_player1_pos = self.move_player(self.player1_pos, self.player1_direction, BLUE)
+        new_player2_pos = self.move_player(self.player2_pos, self.player2_direction, RED)
+
+        if not new_player1_pos or not new_player2_pos:
+            self.running = False
+            return
+
+        self.player1_pos = new_player1_pos
+        self.player2_pos = new_player2_pos
+
+    def ai_move(self, player_pos, player_direction, opponent_pos):
+        def is_valid_move(new_pos):
+            x, y = new_pos
+            return (0 <= x < NUM_TILES_X) and (0 <= y < NUM_TILES_Y) and self.board[y][x] == BLACK
+
+        def simulate_move(pos, direction):
+            x, y = pos
+            dx, dy = direction
+            return (x + dx, y + dy)
+
+        def alpha_beta_search(pos, direction, opponent_pos, depth, alpha, beta, max_player):
+            if depth == 0:
+                return self.heuristic_evaluation(pos, opponent_pos)
+
+            if max_player:
+                value = -float('inf')
+                for d in [UP, DOWN, LEFT, RIGHT]:
+                    new_pos = simulate_move(pos, d)
+                    if is_valid_move(new_pos):
+                        value = max(value, alpha_beta_search(new_pos, d, opponent_pos, depth - 1, alpha, beta, False))
+                        alpha = max(alpha, value)
+                        if beta <= alpha:
+                            break
+                return value
+            else:
+                value = float('inf')
+                for d in [UP, DOWN, LEFT, RIGHT]:
+                    new_pos = simulate_move(opponent_pos, d)
+                    if is_valid_move(new_pos):
+                        value = min(value, alpha_beta_search(pos, direction, new_pos, depth - 1, alpha, beta, True))
+                        beta = min(beta, value)
+                        if beta <= alpha:
+                            break
+                return value
+
+        best_score = -float('inf')
+        best_move = None
+        for d in [UP, DOWN, LEFT, RIGHT]:
+            new_pos = simulate_move(player_pos, d)
+            if is_valid_move(new_pos):
+                score = alpha_beta_search(new_pos, d, opponent_pos, depth=3, alpha=-float('inf'), beta=float('inf'), max_player=False)
+                if score > best_score:
+                    best_score = score
+                    best_move = d
+        return best_move
+
+    def heuristic_evaluation(self, player_pos, opponent_pos):
+        # The heuristic evaluation function calculates the manhattan distance between the player's position and the opponent's position
+        return -(abs(player_pos[0] - opponent_pos[0]) + abs(player_pos[1] - opponent_pos[1]))
+
+    def run(self):
+        while self.running:
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    self.running = False
+                elif event.type == pygame.KEYDOWN:
+                    if event.key == pygame.K_w and self.player1_direction != DOWN:
+                        self.player1_direction = UP
+                    elif event.key == pygame.K_s and self.player1_direction != UP:
+                        self.player1_direction = DOWN
+                    elif event.key == pygame.K_a and self.player1_direction != RIGHT:
+                        self.player1_direction = LEFT
+                    elif event.key == pygame.K_d and self.player1_direction != LEFT:
+                        self.player1_direction = RIGHT
+
+            self.screen.fill(WHITE)
+            self.update()
+            self.draw_board()
+            pygame.display.flip()
+            self.clock.tick(10)
+
+        pygame.quit()
+
+
+if __name__ == "__main__":
+    game = TronGame()
+    game.run()
+