260 lines
12 KiB
Python
260 lines
12 KiB
Python
from snakes.TemplateSnake import TemplateSnake
|
|
from collections import deque
|
|
|
|
class BetterMasterSnake(TemplateSnake):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.name = "BetterMasterSnake"
|
|
# Definiere die möglichen Bewegungsrichtungen
|
|
self.min_safe_area = 2
|
|
|
|
def choose_move(self, game_data):
|
|
move = None
|
|
self.calculations = []
|
|
self.eat_the_snake_overwrite = False
|
|
|
|
self.board_width = game_data['board']['width']
|
|
self.board_height = game_data['board']['height']
|
|
|
|
self.my_snake = game_data['you']
|
|
self.other_snakes = [ x for x in game_data['board']['snakes'] if x["id"] != self.my_snake["id"] ]
|
|
|
|
self.my_head = self.my_snake['head']
|
|
self.my_body = self.my_snake["body"]
|
|
self.food_positions = game_data['board']['food']
|
|
self.game_type = game_data['game']["ruleset"]["name"]
|
|
|
|
self.board = game_data['board']
|
|
|
|
self.find_safe_positions()
|
|
if self.eat_the_snake_overwrite:
|
|
return self.overwrite_eat_the_other_snake(move, game_data["turn"])
|
|
|
|
if self.game_type == "constrictor":
|
|
move = self.selected_move_constrictor()
|
|
else:
|
|
move = self.selected_move_standard()
|
|
|
|
self.add_to_history({"turn": game_data["turn"], "data": self.calculations})
|
|
return move if move else "up"
|
|
|
|
def overwrite_eat_the_other_snake(self, move:str, turn:int):
|
|
if len(self.safe_positions) > 1:
|
|
first_key = list(self.safe_positions.keys())[0]
|
|
self.add_calculations({"function": "eat_the_snake_overwrite", "my_head": self.my_head, "move": move, "safe_positions": self.safe_positions, "selected_move": self.safe_positions[first_key]})
|
|
self.add_to_history({"turn": turn, "data": self.calculations})
|
|
return self.safe_positions[first_key]
|
|
|
|
self.add_calculations({"function": "eat_the_snake_overwrite", "my_head": self.my_head, "move": move, "safe_positions": self.safe_positions})
|
|
self.add_to_history({"turn": turn, "data": self.calculations})
|
|
return self.safe_positions
|
|
|
|
#TODO: How to Fill the Gameboard best?
|
|
def selected_move_constrictor(self):
|
|
move = self.move_close_to_body()
|
|
self.add_calculations({"function": "move_close_to_body", "my_head": self.my_head, "move": move})
|
|
move = self.ensure_escape_route(move)
|
|
self.add_calculations({"function": "ensure_escape_route", "my_head": self.my_head, "move": move, "safe_positions": self.safe_positions})
|
|
return move
|
|
|
|
def selected_move_standard(self, move=None):
|
|
# Finde den besten Weg zur Nahrung
|
|
path_to_food = self.find_path_to_food()
|
|
if path_to_food:
|
|
move = self.move_towards(path_to_food[0])
|
|
self.add_calculations({"function": "move_towards", "my_head": self.my_head, "path_to_food": path_to_food, "move": move})
|
|
|
|
if not move or self.would_eating_the_food_kill_the_snake(move):
|
|
move = self.move_close_to_body(move_close_to_tail=True)
|
|
self.add_calculations({"function": "move_close_to_body", "my_head": self.my_head, "move": move})
|
|
|
|
# Überprfe, ob der Zug einen Ausweg lässt
|
|
move = self.ensure_escape_route(move)
|
|
self.add_calculations({"function": "ensure_escape_route", "my_head": self.my_head, "move": move, "safe_positions": self.safe_positions})
|
|
return move
|
|
|
|
def find_path_to_food(self):
|
|
# Exclude own snake's body from obstacles
|
|
obstacles = set((part['x'], part['y']) for part in self.my_body)
|
|
|
|
for snake in self.other_snakes:
|
|
for part in snake['body']:
|
|
obstacles.add((part['x'], part['y']))
|
|
|
|
# Choose the closest food source based on the heuristic
|
|
closest_food = min(self.food_positions, key=lambda food: abs(food['x'] - self.my_head['x']) + abs(food['y'] - self.my_head['y']))
|
|
|
|
# Use A* to search for a safe path
|
|
path = self.a_star_search(self.my_head, closest_food, obstacles)
|
|
return path
|
|
|
|
def find_path_to_tail(self):
|
|
# Exclude other snake's body from obstacles
|
|
obstacles = set((part['x'], part['y']) for part in self.my_body)
|
|
for snake in self.other_snakes:
|
|
for part in snake['body']:
|
|
obstacles.add((part['x'], part['y']))
|
|
|
|
my_snake_tail = {"x": self.my_body[-1]['x'], "y": self.my_body[-1]['y']}
|
|
|
|
# Use A* to search for a safe path
|
|
path = self.a_star_search(self.my_head, my_snake_tail, obstacles)
|
|
return path
|
|
|
|
def move_towards(self, target):
|
|
best_direction = None
|
|
min_distance = float('inf')
|
|
for direction, coords in self.safe_positions.items():
|
|
distance = abs(target['x'] - coords['x']) + abs(target['y'] - coords['y'])
|
|
if distance < min_distance:
|
|
min_distance = distance
|
|
best_direction = direction
|
|
|
|
return best_direction if best_direction else "up"
|
|
|
|
def move_close_to_body(self, move_close_to_tail=False):
|
|
# Heuristik, um Positionen nahe dem eigenen Körper zu bevorzugen
|
|
body_positions = set((part['x'], part['y']) for part in self.my_body)
|
|
tail_position = (self.my_body[-1]['x'], self.my_body[-1]['y'])
|
|
|
|
best_move = None
|
|
max_distance = -1 # Initialize maximum distance
|
|
for direction, pos in self.safe_positions.items():
|
|
next_position = (pos['x'], pos['y'])
|
|
if next_position in self.safe_positions:
|
|
# Berechne die Distanz zum eigenen Körper
|
|
distance_to_body = min(abs(next_position[0] - part[0]) + abs(next_position[1] - part[1]) for part in body_positions)
|
|
# Berechne die Distanz zum eigenen Schwanz
|
|
distance_to_tail = abs(next_position[0] - tail_position[0]) + abs(next_position[1] - tail_position[1])
|
|
# Wähle die maximale Distanz (Körper oder Schwanz)
|
|
if move_close_to_tail:
|
|
distance = min(next_position, distance_to_tail)
|
|
else:
|
|
distance = max(next_position, distance_to_body)
|
|
# Update max_distance if a larger distance is found
|
|
if distance > max_distance:
|
|
max_distance = distance
|
|
best_move = direction
|
|
return best_move if best_move else "up" # Standardbewegung, falls keine bessere gefunden wird
|
|
|
|
#TODO: Neat to Implement Function to check if eating the food would kill the snake?
|
|
def would_eating_the_food_kill_the_snake(self, move:str):
|
|
return False
|
|
|
|
def ensure_escape_route(self, move):
|
|
try:
|
|
future_position = self.safe_positions[move]
|
|
except KeyError:
|
|
for move, pos in self.safe_positions.items():
|
|
if self.is_near_tail(pos, (self.my_body[-1]['x'], self.my_body[-1]['y'])):
|
|
self.add_calculations({"function": "ensure_escape_route", "move": move, "is_near_tail": True})
|
|
move = self.move_towards(pos)
|
|
return move
|
|
else:
|
|
path_to_tail = self.find_path_to_tail()
|
|
if path_to_tail:
|
|
self.add_calculations({"function": "move_towards", "my_head": self.my_head, "path_to_tail": path_to_tail, "move": move})
|
|
move = self.move_towards(path_to_tail[0])
|
|
|
|
self.add_calculations({"function": "ensure_escape_route", "move": move, "KeyError": "Snake Coild itself up"})
|
|
#return move
|
|
|
|
# TODO: Fix - Snake Neat to find the best way - Close to the Tail and maybe fill most free cells as posible
|
|
#if self.will_end_in_dead_end(self.safe_positions[move], depth=20):
|
|
# return move
|
|
|
|
return move
|
|
|
|
def will_end_in_dead_end(self, start, depth=10):
|
|
start = (start['x'], start['y'])
|
|
return self.dfs_dead_end(self.board, start, depth, set([start]))
|
|
|
|
def dfs_dead_end(self, board, position, depth, path):
|
|
# Abbruchbedingung der Rekursion: Wenn Tiefe erreicht ist
|
|
if depth == 0:
|
|
return False # Nicht genügend Tiefe, um eine Sackgasse zu bestätigen
|
|
|
|
# Bewege nach oben
|
|
next_position = position
|
|
if next_position[0] < 0 or next_position in board['snakes'] or next_position in path:
|
|
return True # Sackgasse gefunden
|
|
|
|
# Füge aktuelle Position zum Pfad hinzu
|
|
path.add(next_position)
|
|
# Rekursive Überprüfung der nächsten Position
|
|
result = self.dfs_dead_end(board, next_position, depth - 1, path)
|
|
# Entferne aktuelle Position vom Pfad
|
|
path.remove(next_position)
|
|
return result
|
|
|
|
def is_position_safe(self, position):
|
|
return 0 <= position['x'] < self.board_width and 0 <= position['y'] < self.board_height and (position['x'], position['y']) not in self.my_body
|
|
|
|
def is_near_tail(self, position, tail):
|
|
return abs(position["x"] - tail[0]) + abs(position["y"] - tail[1]) <= 2
|
|
|
|
def a_star_search(self, start, goal, obstacles):
|
|
# Helper functions
|
|
def is_position_safe(position):
|
|
return 0 <= position['x'] < self.board_width and 0 <= position['y'] < self.board_height and (position['x'], position['y']) not in obstacles
|
|
|
|
def get_neighbors(position):
|
|
neighbors = []
|
|
for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]: # links, rechts, oben, unten
|
|
neighbor = {'x': position['x'] + dx, 'y': position['y'] + dy}
|
|
if is_position_safe(neighbor):
|
|
neighbors.append(neighbor)
|
|
return neighbors
|
|
|
|
def heuristic(position, goal):
|
|
# Verwenden Sie eine Heuristik, die immer positiv ist, selbst wenn das Ziel in der Nähe ist
|
|
return max(abs(position['x'] - goal['x']), abs(position['y'] - goal['y']))
|
|
|
|
# Überprüfen, ob das Ziel direkt neben dem Startpunkt liegt
|
|
if start == goal or (abs(start['x'] - goal['x']) <= 1 and abs(start['y'] - goal['y']) <= 1):
|
|
# Wenn das Ziel neben dem Startpunkt liegt, ist der Pfad das Ziel selbst
|
|
return [goal]
|
|
|
|
# Initialize the open and closed list
|
|
open_set = set([(start['x'], start['y'])])
|
|
came_from = {}
|
|
g_score = {(start['x'], start['y']): 0}
|
|
f_score = {(start['x'], start['y']): heuristic(start, goal)}
|
|
|
|
while open_set:
|
|
current = min(open_set, key=lambda pos: f_score.get(pos, float('inf')))
|
|
current_dict = {'x': current[0], 'y': current[1]}
|
|
if current_dict == goal:
|
|
# Reconstruct the path
|
|
path = []
|
|
while current in came_from:
|
|
current = came_from[current]
|
|
path.append({'x': current[0], 'y': current[1]})
|
|
path.reverse()
|
|
if path and path[0] == start:
|
|
path.pop(0) # Entferne das erste Element, wenn es dem Start entspricht
|
|
return path # Return the path as a list of dicts
|
|
|
|
open_set.remove(current)
|
|
for neighbor in get_neighbors(current_dict):
|
|
neighbor_tuple = (neighbor['x'], neighbor['y'])
|
|
tentative_g_score = g_score[current] + 1 # Distance between neighbors is always 1
|
|
if tentative_g_score < g_score.get(neighbor_tuple, float('inf')):
|
|
came_from[neighbor_tuple] = current
|
|
g_score[neighbor_tuple] = tentative_g_score
|
|
f_score[neighbor_tuple] = g_score[neighbor_tuple] + heuristic(neighbor, goal)
|
|
if neighbor_tuple not in open_set:
|
|
open_set.add(neighbor_tuple)
|
|
|
|
return None # Kein Pfad gefunden
|
|
|
|
def find_direction(self):
|
|
# Beispielhafte Logik zur Auswahl einer Bewegungsrichtung
|
|
for direction, pos in self.safe_positions.items():
|
|
next_position = (pos['x'], pos['y'])
|
|
# Konvertiere safe_positions in eine Liste von Tupeln für den Vergleich
|
|
safe_positions_tuples = [(pos['x'], pos['y']) for pos in self.safe_positions.values()]
|
|
if next_position in safe_positions_tuples:
|
|
return direction
|
|
return "up" # Standardbewegung, falls keine sichere Position gefunden wird
|