226 lines
10 KiB
Python
226 lines
10 KiB
Python
from snakes.TemplateSnake import TemplateSnake
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from collections import deque
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class BetterMasterSnake(TemplateSnake):
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def __init__(self):
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super().__init__()
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self.name = "BetterMasterSnake"
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# Definiere die möglichen Bewegungsrichtungen
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self.min_safe_area = 2
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def choose_move(self, game_data):
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move = None
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self.calculations = []
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self.eat_the_snake_overwrite = False
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self.board_width = game_data['board']['width']
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self.board_height = game_data['board']['height']
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self.my_snake = game_data['you']
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self.other_snakes = [ x for x in game_data['board']['snakes'] if x["id"] != self.my_snake["id"] ]
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self.my_head = self.my_snake['head']
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self.my_body = self.my_snake["body"]
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self.food_positions = game_data['board']['food']
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self.game_type = game_data['game']["ruleset"]["name"]
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self.board = game_data['board']
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self.find_safe_positions()
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if self.eat_the_snake_overwrite:
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return self.overwrite_eat_the_other_snake(game_data["turn"])
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if self.game_type == "constrictor":
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move = self.selected_move_constrictor()
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else:
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move = self.selected_move_standard()
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self.add_to_history({"turn": game_data["turn"], "data": self.calculations})
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return move if move else "up"
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def overwrite_eat_the_other_snake(self, turn:int):
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self.add_calculations({"function": "eat_the_snake_overwrite", "my_head": self.my_head, "move": self.kill_the_snake, "safe_positions": self.safe_positions})
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self.add_to_history({"turn": turn, "data": self.calculations})
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return self.kill_the_snake
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#TODO: How to Fill the Gameboard best?
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def selected_move_constrictor(self):
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move = self.move_close_to_body()
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self.add_calculations({"function": "move_close_to_body", "my_head": self.my_head, "move": move})
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move = self.ensure_escape_route(move)
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self.add_calculations({"function": "ensure_escape_route", "my_head": self.my_head, "move": move, "safe_positions": self.safe_positions})
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return move
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def selected_move_standard(self, move=None):
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# Finde den besten Weg zur Nahrung
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path_to_food = self.find_path_to_food()
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if path_to_food:
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move = self.move_towards(path_to_food[0])
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self.add_calculations({"function": "move_towards", "my_head": self.my_head, "path_to_food": path_to_food, "move": move})
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if not move or self.would_eating_the_food_kill_the_snake(move):
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move = self.move_close_to_body(move_close_to_tail=True)
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self.add_calculations({"function": "move_close_to_body", "my_head": self.my_head, "move": move})
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# Überprfe, ob der Zug einen Ausweg lässt
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move = self.ensure_escape_route(move)
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self.add_calculations({"function": "ensure_escape_route", "my_head": self.my_head, "move": move, "safe_positions": self.safe_positions})
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return move
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def find_path_to_food(self):
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# Exclude own snake's body from obstacles
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obstacles = set((part['x'], part['y']) for part in self.my_body)
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for snake in self.other_snakes:
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for part in snake['body']:
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obstacles.add((part['x'], part['y']))
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# Choose the closest food source based on the heuristic
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closest_food = min(self.food_positions, key=lambda food: abs(food['x'] - self.my_head['x']) + abs(food['y'] - self.my_head['y']))
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# Use A* to search for a safe path
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path = self.a_star_search(self.my_head, closest_food, obstacles)
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return path
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def find_path_to_tail(self):
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# Exclude other snake's body from obstacles
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obstacles = set((part['x'], part['y']) for part in self.my_body)
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for snake in self.other_snakes:
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for part in snake['body']:
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obstacles.add((part['x'], part['y']))
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my_snake_tail = {"x": self.my_body[-1]['x'], "y": self.my_body[-1]['y']}
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# Use A* to search for a safe path
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path = self.a_star_search(self.my_head, my_snake_tail, obstacles)
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return path
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def move_towards(self, target):
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best_direction = None
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min_distance = float('inf')
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for direction, coords in self.safe_positions.items():
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distance = abs(target['x'] - coords['x']) + abs(target['y'] - coords['y'])
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if distance < min_distance:
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min_distance = distance
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best_direction = direction
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return best_direction if best_direction else "up"
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def move_close_to_body(self, move_close_to_tail=False):
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# Heuristik, um Positionen nahe dem eigenen Körper zu bevorzugen
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body_positions = set((part['x'], part['y']) for part in self.my_body)
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tail_position = (self.my_body[-1]['x'], self.my_body[-1]['y'])
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best_move = None
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max_distance = -1 # Initialize maximum distance
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for direction, pos in self.safe_positions.items():
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next_position = (pos['x'], pos['y'])
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if next_position in self.safe_positions:
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# Berechne die Distanz zum eigenen Körper
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distance_to_body = min(abs(next_position[0] - part[0]) + abs(next_position[1] - part[1]) for part in body_positions)
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# Berechne die Distanz zum eigenen Schwanz
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distance_to_tail = abs(next_position[0] - tail_position[0]) + abs(next_position[1] - tail_position[1])
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# Wähle die maximale Distanz (Körper oder Schwanz)
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if move_close_to_tail:
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distance = min(next_position, distance_to_tail)
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else:
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distance = max(next_position, distance_to_body)
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# Update max_distance if a larger distance is found
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if distance > max_distance:
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max_distance = distance
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best_move = direction
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return best_move if best_move else "up" # Standardbewegung, falls keine bessere gefunden wird
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#TODO: Neat to Implement Function to check if eating the food would kill the snake?
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def would_eating_the_food_kill_the_snake(self, move:str):
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return False
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def ensure_escape_route(self, move:str):
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try:
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future_position = self.safe_positions[move]
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except KeyError:
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for move, pos in self.safe_positions.items():
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if self.is_near_tail(pos, (self.my_body[-1]['x'], self.my_body[-1]['y'])):
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self.add_calculations({"function": "ensure_escape_route", "move": move, "is_near_tail": True})
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move = self.move_towards(pos)
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return move
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else:
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path_to_tail = self.find_path_to_tail()
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if path_to_tail:
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self.add_calculations({"function": "move_towards", "my_head": self.my_head, "path_to_tail": path_to_tail, "move": move})
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move = self.move_towards(path_to_tail[0])
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self.add_calculations({"function": "ensure_escape_route", "move": move, "KeyError": "Snake Coild itself up"})
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#return move
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# TODO: Fix - Snake Neat to find the best way - Close to the Tail and maybe fill most free cells as posible
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return move
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def is_near_tail(self, position, tail):
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return abs(position["x"] - tail[0]) + abs(position["y"] - tail[1]) <= 2
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def a_star_search(self, start, goal, obstacles):
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# Helper functions
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def is_position_safe(position):
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return 0 <= position['x'] < self.board_width and 0 <= position['y'] < self.board_height and (position['x'], position['y']) not in obstacles
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def get_neighbors(position):
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neighbors = []
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for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]: # links, rechts, oben, unten
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neighbor = {'x': position['x'] + dx, 'y': position['y'] + dy}
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if is_position_safe(neighbor):
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neighbors.append(neighbor)
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return neighbors
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def heuristic(position, goal):
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# Verwenden Sie eine Heuristik, die immer positiv ist, selbst wenn das Ziel in der Nähe ist
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return max(abs(position['x'] - goal['x']), abs(position['y'] - goal['y']))
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# Überprüfen, ob das Ziel direkt neben dem Startpunkt liegt
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if start == goal or (abs(start['x'] - goal['x']) <= 1 and abs(start['y'] - goal['y']) <= 1):
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# Wenn das Ziel neben dem Startpunkt liegt, ist der Pfad das Ziel selbst
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return [goal]
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# Initialize the open and closed list
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open_set = set([(start['x'], start['y'])])
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came_from = {}
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g_score = {(start['x'], start['y']): 0}
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f_score = {(start['x'], start['y']): heuristic(start, goal)}
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while open_set:
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current = min(open_set, key=lambda pos: f_score.get(pos, float('inf')))
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current_dict = {'x': current[0], 'y': current[1]}
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if current_dict == goal:
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# Reconstruct the path
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path = []
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while current in came_from:
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current = came_from[current]
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path.append({'x': current[0], 'y': current[1]})
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path.reverse()
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if path and path[0] == start:
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path.pop(0) # Entferne das erste Element, wenn es dem Start entspricht
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return path # Return the path as a list of dicts
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open_set.remove(current)
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for neighbor in get_neighbors(current_dict):
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neighbor_tuple = (neighbor['x'], neighbor['y'])
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tentative_g_score = g_score[current] + 1 # Distance between neighbors is always 1
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if tentative_g_score < g_score.get(neighbor_tuple, float('inf')):
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came_from[neighbor_tuple] = current
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g_score[neighbor_tuple] = tentative_g_score
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f_score[neighbor_tuple] = g_score[neighbor_tuple] + heuristic(neighbor, goal)
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if neighbor_tuple not in open_set:
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open_set.add(neighbor_tuple)
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return None # Kein Pfad gefunden
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def find_direction(self):
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# Beispielhafte Logik zur Auswahl einer Bewegungsrichtung
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for direction, pos in self.safe_positions.items():
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next_position = (pos['x'], pos['y'])
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# Konvertiere safe_positions in eine Liste von Tupeln für den Vergleich
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safe_positions_tuples = [(pos['x'], pos['y']) for pos in self.safe_positions.values()]
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if next_position in safe_positions_tuples:
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return direction
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return "up" # Standardbewegung, falls keine sichere Position gefunden wird
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