Merge pull request 'Genetic-algorythm' (#29) from Genetic-algorythm into main

Reviewed-on: #29
Reviewed-by: Nastassia Zhuravel <naszhu@st.amu.edu.pl>

.gitignore

@@ -4,4 +4,5 @@
 __pycache__
 
 #PyCharm
-.idea/
+.idea/
+AI_brain/model.h5

AI_brain/genetic_algorytm.py

@@ -0,0 +1,281 @@
+import copy
+import random
+import configparser
+import math
+
+import pygame
+from domain.entities.entity import Entity
+
+
+config = configparser.ConfigParser()
+config.read("config.ini")
+
+from domain.world import World
+from AI_brain.rotate_and_go_aStar import RotateAndGoAStar, State
+from random import randint
+
+
+hits = 0
+misses = 0
+
+
+class Cashed_sub_paths(dict):
+    def __init__(self):
+        super().__init__()
+
+    def __missing__(self, key):
+        self[key] = Cashed_sub_paths()
+        return self[key]
+
+
+class Cashed_sub_path:
+    def __init__(self, sub_path: list[str] = [], distance: int = 0):
+        self.sub_path = sub_path
+        self.distance = distance
+
+
+steps_distance_cashed: dict[tuple[int, int], Cashed_sub_path] = Cashed_sub_paths()
+
+
+class Path:
+    def __init__(self):
+        self.walk = []
+        self.permutation = []
+        self.real_path = []
+        self.distance = 0
+
+    def random_walk(self, dusts: list[Entity]):
+        permutation = generate_random_permutation(len(dusts))
+        self.permutation = permutation
+
+        self.walk = addStartAndStation(permutation)
+
+    def calculate_distance(self, world: World):
+        distance = 0
+        for i in range(len(self.walk) - 1):
+            next_distance, next_real_path = self.step_distance(
+                self.walk[i], self.walk[i + 1], world
+            )
+            distance += next_distance
+
+            # BUG this part is not working and is not used, B.1 must be resolved
+            self.real_path = self.real_path + ["DEFAULT_ROTATION"] + next_real_path
+
+        self.distance = distance
+
+    def step_distance(
+        self, from_id: int, to_id: int, world: World
+    ) -> tuple[int, list[str]]:
+        global hits, misses
+        if (from_id, to_id) in steps_distance_cashed:
+            hits += 1
+            distance = steps_distance_cashed[(from_id, to_id)].distance
+            sub_path = steps_distance_cashed[(from_id, to_id)].sub_path
+            return distance, sub_path
+
+        misses += 1
+        path_searcher = RotateAndGoAStar(
+            world,
+            self.getPosition(from_id, world.dustList),
+            self.getPosition(to_id, world.dustList),
+        )
+        path_searcher.search()
+
+        steps_distance_cashed[(from_id, to_id)] = Cashed_sub_path(
+            path_searcher.actions, path_searcher.cost
+        )
+
+        # BUG B.1 inverse path
+        inverse_sub_path = path_searcher.actions.copy()
+        steps_distance_cashed[(to_id, from_id)] = Cashed_sub_path(
+            inverse_sub_path, path_searcher.cost
+        )
+        return path_searcher.cost, path_searcher.actions
+
+    def inverse_sub_path(sub_path: list[str]) -> list[str]:
+        sub_path.reverse()
+        for command in sub_path:
+            command.replace("RL", "RR")
+            command.replace("RR", "RR")
+
+    def getPosition(
+        self,
+        number: int,
+        dustList: list[Entity],
+    ) -> State:
+        if number == -1:
+            dock_start_x, dock_start_y = config.get(
+                "CONSTANT", "DockStationStartPosition"
+            ).split(",")
+            dock_start_x, dock_start_y = int(dock_start_x), int(dock_start_y)
+
+            return State(dock_start_x, dock_start_y)
+
+        if number == -2:
+            vacuum_start_x, vacuum_start_y = config.get(
+                "CONSTANT", "RobotStartPosition"
+            ).split(",")
+
+            vacuum_start_x, vacuum_start_y = int(vacuum_start_x), int(vacuum_start_y)
+            return State(vacuum_start_x, vacuum_start_y)
+
+        return State(dustList[number].x, dustList[number].y)
+
+    def get_real_path(self, world: World):
+        full_path = []
+
+        for index_place in range(len(self.walk) - 1):
+            path_searcher = RotateAndGoAStar(
+                world,
+                self.getPosition(self.walk[index_place], world.dustList),
+                self.getPosition(self.walk[index_place + 1], world.dustList),
+            )
+            path_searcher.search()
+            full_path = full_path + ["DEFAULT_ROTATION"] + path_searcher.actions
+
+        self.real_path = full_path
+
+
+def generate_random_permutation(n):
+    # Create a list of numbers from 1 to n
+    numbers = list(range(0, n))
+
+    # Shuffle the list using the random.shuffle function
+    random.shuffle(numbers)
+
+    return numbers
+
+
+# BUG solution: inverse direction at the last step
+def addStartAndStation(permutation: list[int]):
+    frequency = math.ceil(100 / config.getint("CONSTANT", "BananaFilling"))
+    numer_of_stops = math.ceil(len(permutation) / frequency)
+    walk = permutation.copy()
+
+    for i in range(1, numer_of_stops):
+        walk.insert((frequency + 1) * i - 1, -1)
+    walk.insert(len(walk), -1)
+    walk.insert(0, -2)
+
+    return walk
+
+
+class GeneticAlgorytm:
+    def __init__(self, world: World):
+        self.world = world
+        self.population_size = config.getint("GENETIC_ALGORITHM", "PopulationSize")
+        self.mutation_probability = config.getfloat(
+            "GENETIC_ALGORITHM", "MutationProbability"
+        )
+        self.iteration_number = config.getint("GENETIC_ALGORITHM", "IterationNumber")
+        self.descendants_number = config.getint(
+            "GENETIC_ALGORITHM", "DescendantsNumber"
+        )
+        self.dusts = world.dustList
+        self.doc_station = world.doc_station
+        self.paths: list[Path] = []
+        self.checked_permutations = {}
+
+        self.best_path = None
+        self.best_distance = math.inf
+        self.best_real_path = []
+
+    def generate_population(self):
+        for i in range(self.population_size):
+            path = Path()
+            path.random_walk(self.dusts)
+            self.checked_permutations[tuple(path.permutation)] = True
+            path.calculate_distance(self.world)
+            self.paths.append(path)
+
+    def print_top(self):
+        print(
+            "Best path: ",
+            self.best_path.walk,
+            "Distance: ",
+            self.best_path.distance,
+        )
+        for path in self.paths[1:]:
+            print(path.walk, path.distance)
+
+    def evaluate_population(self):
+        self.paths.sort(key=lambda x: x.distance, reverse=False)
+
+        self.best_distance = self.paths[0].distance
+        self.best_path = self.paths[0]
+
+        for path in self.paths[self.population_size :]:
+            del self.checked_permutations[tuple(path.permutation)]
+
+        self.paths = self.paths[: self.population_size]
+
+    def create_child(self, parent1: Path, parent2: Path) -> Path:
+        child = Path()
+
+        child.permutation = parent1.permutation[: len(parent1.permutation) // 2]
+
+        # Add missing items from parent2 in the order they appear
+        for item in parent2.permutation:
+            if item not in child.permutation:
+                child.permutation.append(item)
+
+        child.walk = addStartAndStation(child.permutation)
+
+        child.calculate_distance(self.world)
+
+        return child
+
+    def run(self):
+        self.generate_population()
+
+        for i in range(self.iteration_number):
+            self.crossover()
+
+        self.evaluate_population()
+        self.best_real_path = self.paths[0].get_real_path(self.world)
+
+        print(hits, (misses + hits))
+
+        print(hits / (misses + hits))
+
+    def mutate(self, mutant: Path) -> Path:
+        random_number = randint(0, len(mutant.permutation) - 1)
+        random_number2 = random_number
+        while random_number == random_number2:
+            random_number2 = randint(0, len(mutant.permutation) - 1)
+
+        mutant.permutation[random_number], mutant.permutation[random_number2] = (
+            mutant.permutation[random_number2],
+            mutant.permutation[random_number],
+        )
+
+        if tuple(mutant.permutation) in self.checked_permutations:
+            return self.mutate(mutant)
+
+        mutant.walk = addStartAndStation(mutant.permutation)
+
+        mutant.calculate_distance(self.world)
+        return mutant
+
+    def crossover(self):
+        for i in range(self.descendants_number):
+            parent1 = self.paths[random.randint(0, self.population_size - 1)]
+
+            parent2 = self.paths[random.randint(0, self.population_size - 1)]
+
+            child = self.create_child(parent1, parent2)
+            while tuple(child.permutation) in self.checked_permutations:
+                parent1 = self.paths[random.randint(0, self.population_size - 1)]
+                parent2 = self.paths[random.randint(0, self.population_size - 1)]
+                child = self.create_child(parent1, parent2)
+
+            self.checked_permutations[tuple(child.permutation)] = True
+            self.paths.append(child)
+
+            mutant = Path()
+            mutant.permutation = child.permutation.copy()
+            mutant = self.mutate(mutant)
+            self.checked_permutations[tuple(mutant.permutation)] = True
+            self.paths.append(mutant)
+
+        self.evaluate_population()

AI_brain/image_recognition.py

@@ -5,18 +5,19 @@ from tensorflow import keras
 import cv2
 import random
 
-#You can download model from https://uam-my.sharepoint.com/:f:/g/personal/pavbia_st_amu_edu_pl/EmBHjnETuk5LiCZS6xk7AnIBNsnffR3Sygf8EX2bhR1w4A
-#Change the path to model + to datasets (string 12 + strings 35,41,47,53)
+# You can download model from https://uam-my.sharepoint.com/:f:/g/personal/pavbia_st_amu_edu_pl/EmBHjnETuk5LiCZS6xk7AnIBNsnffR3Sygf8EX2bhR1w4A
+# Change the path to model + to datasets (string 12 + strings 35,41,47,53)
+
 
 class VacuumRecognizer:
-    model = keras.models.load_model('AI_brain\model.h5') #Neuron model path
+    model = keras.models.load_model("AI_brain\model.h5")  # Neuron model path
 
     def recognize(self, image_path) -> str:
-        class_names = ['Banana', 'Cat', 'Earings', 'Plant']
+        class_names = ["Banana", "Cat", "Earings", "Plant"]
 
         img = cv2.imread(image_path, flags=cv2.IMREAD_GRAYSCALE)
         cv2.waitKey(0)
-        img = (np.expand_dims(img, 0))
+        img = np.expand_dims(img, 0)
 
         predictions = self.model.predict(img)[0].tolist()
 
@@ -31,31 +32,43 @@ class VacuumRecognizer:
         return class_names[predictions.index(max(predictions))]
 
     def get_random_dir(self, type) -> str:
-        if type == 'Plant':
-            plant_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant' #Plant dataset path
+        if type == "Plant":
+            plant_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Plant"  # Plant dataset path
             plant_dirs = os.listdir(plant_image_paths)
-            full_path = plant_image_paths + '\\' + plant_dirs[random.randint(0, len(plant_dirs)-1)]
+            full_path = (
+                plant_image_paths
+                + "\\"
+                + plant_dirs[random.randint(0, len(plant_dirs) - 1)]
+            )
             print(full_path)
             return full_path
-        elif type == 'Earings':
-            earnings_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings' #Earings dataset path
+        elif type == "Earings":
+            earnings_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings"  # Earings dataset path
             earning_dirs = os.listdir(earnings_image_paths)
-            full_path = earnings_image_paths + '\\' + earning_dirs[random.randint(0, len(earning_dirs)-1)]
+            full_path = (
+                earnings_image_paths
+                + "\\"
+                + earning_dirs[random.randint(0, len(earning_dirs) - 1)]
+            )
             print(full_path)
             return full_path
-        elif type == 'Banana':
-            banana_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana' #Banana dataset path
+        elif type == "Banana":
+            banana_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana"  # Banana dataset path
             banana_dirs = os.listdir(banana_image_paths)
-            full_path = banana_image_paths + '\\' + banana_dirs[random.randint(0, len(banana_dirs)-1)]
+            full_path = (
+                banana_image_paths
+                + "\\"
+                + banana_dirs[random.randint(0, len(banana_dirs) - 1)]
+            )
             print(full_path)
             return full_path
-        elif type == 'Cat':
-            cat_image_paths = 'C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat' #Cat dataset path
+        elif type == "Cat":
+            cat_image_paths = "C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat"  # Cat dataset path
             cat_dir = os.listdir(cat_image_paths)
 
 
-#For testing the neuron model
-'''image_paths = []
+# For testing the neuron model
+"""image_paths = []
 image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Banana')
 image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Cat')
 image_paths.append('C:\\Users\\Pavel\\Desktop\\AI\\Machine_learning_2023\\AI_brain\\Image_datasetJPGnewBnW\\check\\Earings')
@@ -65,4 +78,4 @@ uio = VacuumRecognizer()
 for image_path in image_paths:
     dirs = os.listdir(image_path)
     for i in range(3):
-        print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))'''
+        print(uio.recognize(image_path + '\\' + dirs[random.randint(0, len(dirs)-1)]))"""

AI_brain/rotate_and_go_aStar.py

@@ -3,11 +3,10 @@ from domain.world import World
 
 
 class State:
-    def __init__(self, x, y, direction=(1, 0), entity=None):
+    def __init__(self, x: int, y: int, direction=(1, 0), entity=None):
         self.x = x
         self.y = y
         self.direction = direction
-        
 
     def __hash__(self):
         return hash((self.x, self.y))
@@ -19,7 +18,7 @@ class State:
             and self.direction == other.direction
         )
 
-    def heuristic(self, goal_state):
+    def heuristic(self, goal_state) -> int:
         return abs(self.x - goal_state.x) + abs(self.y - goal_state.y)
 
 
@@ -53,19 +52,19 @@ class RotateAndGoAStar:
         self.enqueued_states = set()
         self.explored = set()
         self.actions = []
+        self.cost = 0
 
-    def get_g_score(self, state):
+    def get_g_score(self, state) -> int:
         return self.world.get_cost(state.x, state.y)
 
     def search(self):
-        heapq.heappush(
-            self.fringe, Node(self.start_state, 0, self.goal_state)
-        )
+        heapq.heappush(self.fringe, Node(self.start_state, 0, self.goal_state))
 
         while self.fringe:
-            elem = heapq.heappop(self.fringe)
+            elem: Node = heapq.heappop(self.fringe)
             if self.is_goal(elem.state):
                 self.actions = action_sequence(elem)
+                self.cost = elem.g_score
                 return True
             self.explored.add(elem.state)
 
@@ -73,7 +72,7 @@ class RotateAndGoAStar:
                 if state in self.explored:
                     continue
 
-                new_g_score = new_g_score = elem.g_score + self.world.get_cost(state.x, state.y)
+                new_g_score = elem.g_score + self.world.get_cost(state.x, state.y)
                 if state not in self.enqueued_states:
                     next_node = Node(state, new_g_score, self.goal_state)
                     next_node.action = action
@@ -84,12 +83,12 @@ class RotateAndGoAStar:
                     for node in self.fringe:
                         if node.state == state:
                             node.g_score = new_g_score
-                            node.f_score = (
-                                new_g_score + node.state.heuristic(self.goal_state)
+                            node.f_score = new_g_score + node.state.heuristic(
+                                self.goal_state
                             )
                             node.parent = elem
                             node.action = action
-                            heapq.heapify(self.fringe)  
+                            heapq.heapify(self.fringe)
                             break
 
         return False
@@ -102,12 +101,12 @@ class RotateAndGoAStar:
         next_x = state.x + state.direction[0]
         next_y = state.y + state.direction[1]
         if self.world.accepted_move(next_x, next_y):
-            new_successors.append(
-                ("GO", State(next_x, next_y, state.direction))
-            )
+            new_successors.append(("GO", State(next_x, next_y, state.direction)))
         return new_successors
-   
+
     def is_goal(self, state: State) -> bool:
-        return (
-                state.x == self.goal_state.x
-                and state.y == self.goal_state.y )
+        return state.x == self.goal_state.x and state.y == self.goal_state.y
+
+    def number_of_moves_forward(self) -> int:
+        go_count = self.actions.count("GO")
+        return go_count

config.ini

@@ -4,9 +4,23 @@ movement = robot
 #accept: human, robot
 
 [CONSTANT]
-NumberOfBananas = 10
-NumberOfEarrings = 3
+NumberOfBananas = 15
+NumberOfEarrings = 0
 NumberOfPlants = 5
+BananaFilling = 25
+RobotStartPosition = 5, 5
+DockStationStartPosition = 5, 6
+#9,8
 
 [NEURAL_NETWORK]
-is_nural_network_off = True
+is_neural_network_off = True
+
+[AI_BRAIN]
+mode = full_clean 
+#accept: full_clean, to_station
+
+[GENETIC_ALGORITHM]
+PopulationSize = 20
+DescendantsNumber = 6
+MutationProbability = 0.3
+IterationNumber = 1_000

domain/commands/vacuum_move_command.py

@@ -21,12 +21,13 @@ class VacuumMoveCommand(Command):
         if not self.world.accepted_move(end_x, end_y):
             return
 
-        tmp = self.world.is_garbage_at(end_x, end_y)
-        if len(tmp) > 0:
-            for t in tmp:
+        garbage = self.world.garbage_at(end_x, end_y)
+        if len(garbage) > 0:
+            for item in garbage:
                 if self.vacuum.get_container_filling() < 100:
                     self.vacuum.increase_container_filling()
-                    self.world.dust[end_x][end_y].remove(t)
+                    self.world.delete_entities_at_Of_type(item.x, item.y, item.type)
+                    self.world.dust[end_x][end_y].remove(item)
 
         if self.world.is_docking_station_at(end_x, end_y):
             self.vacuum.dump_trash()

domain/entities/vacuum.py

@@ -1,5 +1,9 @@
 from domain.entities.entity import Entity
 from domain.world import World
+import configparser
+
+config = configparser.ConfigParser()
+config.read("config.ini")
 
 
 class Vacuum(Entity):
@@ -11,7 +15,7 @@ class Vacuum(Entity):
         self.container_filling = 0
 
     def increase_container_filling(self) -> None:
-        self.container_filling += 5
+        self.container_filling += config.getint("CONSTANT", "BananaFilling")
 
     def dump_trash(self) -> None:
         self.container_filling = 0

domain/world.py

@@ -8,7 +8,9 @@ class World:
         self.width = width
         self.height = height
         self.dust = [[[] for j in range(height)] for i in range(width)]
+        self.dustList: list[Entity] = []
         self.obstacles = [[[] for j in range(height)] for i in range(width)]
+        self.entity = [[[] for j in range(height)] for i in range(width)]
 
         self.vacuum = None
         self.cat = None
@@ -19,8 +21,10 @@ class World:
             self.doc_station = entity
         elif entity.type == "PEEL":
             self.dust[entity.x][entity.y].append(entity)
+            self.dustList.append(Entity(entity.x, entity.y, "PEEL"))
         elif entity.type == "EARRING":
             self.dust[entity.x][entity.y].append(entity)
+            self.dustList.append(Entity(entity.x, entity.y, "EARRING"))
         elif entity.type == "VACUUM":
             self.vacuum = entity
         elif entity.type == "CAT":
@@ -29,10 +33,27 @@ class World:
         else:
             self.obstacles[entity.x][entity.y].append(entity)
 
+        self.entity[entity.x][entity.y].append(entity)
+
+    def is_entity_at(
+        self,
+        x: int,
+        y: int,
+    ) -> bool:
+        if len(self.entity[x][y]) > 0:
+            return True
+        return False
+
+    def delete_entities_at_Of_type(self, x: int, y: int, type: str):
+        entities = self.entity[x][y]
+        for entity in entities:
+            if entity.type == type:
+                entities.remove(entity)
+
     def is_obstacle_at(self, x: int, y: int) -> bool:
         return bool(self.obstacles[x][y])
 
-    def is_garbage_at(self, x: int, y: int):
+    def garbage_at(self, x: int, y: int) -> list[Entity]:
         if len(self.dust[x][y]) == 0:
             return []
         return [i for i in self.dust[x][y] if evaluate([i.properties])[0] == 1]
@@ -54,5 +75,5 @@ class World:
 
         return True
 
-    def get_cost(self, x, y):
+    def get_cost(self, x, y) -> float:
         return self.costs[x][y]

main.py

@@ -3,6 +3,9 @@ from random import randint
 import pygame
 import configparser
 
+config = configparser.ConfigParser()
+config.read("config.ini")
+
 from domain.commands.random_cat_move_command import RandomCatMoveCommand
 from domain.commands.vacuum_move_command import VacuumMoveCommand
 from domain.entities.cat import Cat
@@ -13,17 +16,16 @@ from domain.entities.earring import Earring
 from domain.entities.docking_station import Doc_Station
 from domain.world import World
 from view.renderer import Renderer
-from AI_brain.image_recognition import VacuumRecognizer
+from AI_brain.genetic_algorytm import GeneticAlgorytm, Path
+
+if not config.getboolean("NEURAL_NETWORK", "is_neural_network_off"):
+    from AI_brain.image_recognition import VacuumRecognizer
 
 # from AI_brain.movement import GoAnyDirectionBFS, State
 # from AI_brain.rotate_and_go_bfs import RotateAndGoBFS, State
 from AI_brain.rotate_and_go_aStar import RotateAndGoAStar, State
 
 
-config = configparser.ConfigParser()
-config.read("config.ini")
-
-
 class Main:
     def __init__(self):
         tiles_x = 10
@@ -51,24 +53,38 @@ class Main:
     def run_robot(self):
         self.renderer.render(self.world)
 
-        start_state = State(self.world.vacuum.x, self.world.vacuum.y)
-        end_state = State(self.world.doc_station.x, self.world.doc_station.y)
+        if config["AI_BRAIN"]["mode"] == "to_station":
+            start_state = State(self.world.vacuum.x, self.world.vacuum.y)
+            end_state = State(self.world.doc_station.x, self.world.doc_station.y)
 
-        # path_searcher = GoAnyDirectionBFS(self.world, start_state, end_state)
-        # path_searcher = RotateAndGoBFS(self.world, start_state, end_state)
-        path_searcher = RotateAndGoAStar(self.world, start_state, end_state)
-        if not path_searcher.search():
-            print("No solution")
+            path_searcher = RotateAndGoAStar(self.world, start_state, end_state)
+
+            if not path_searcher.search():
+                print("No solution")
+                exit(0)
+            print(path_searcher.actions)
+            print(path_searcher.cost)
+            robot_actions = path_searcher.actions
+
+        elif config["AI_BRAIN"]["mode"] == "full_clean":
+            genetic_searcher = GeneticAlgorytm(self.world)
+            genetic_searcher.run()
+
+            genetic_searcher.print_top()
+
+            robot_actions = genetic_searcher.best_path.real_path
+
+        else:
+            print("Wrong mode")
             exit(0)
 
-        path_searcher.actions.reverse()
         while self.running:
             for event in pygame.event.get():
                 if event.type == pygame.QUIT:
                     self.running = False
 
-            if len(path_searcher.actions) > 0:
-                action_direction = path_searcher.actions.pop()
+            if len(robot_actions) > 0:
+                action_direction = robot_actions.pop(0)
                 # self.handle_action1(action_direction)
                 self.handle_action2(action_direction)
 
@@ -113,6 +129,8 @@ class Main:
                 self.world.vacuum.direction[1],
                 -self.world.vacuum.direction[0],
             )
+        elif action == "DEFAULT_ROTATION":
+            self.world.vacuum.direction = (1, 0)
 
     def process_input(self):
         for event in pygame.event.get():
@@ -145,35 +163,59 @@ class Main:
 
 
 def generate_world(tiles_x: int, tiles_y: int) -> World:
-    if config.getboolean("NEURAL_NETWORK", "is_nural_network_off"):
+    if config.getboolean("NEURAL_NETWORK", "is_neural_network_off"):
         world = World(tiles_x, tiles_y)
-        for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
-            temp_x = randint(0, tiles_x - 1)
-            temp_y = randint(0, tiles_y - 1)
-            world.add_entity(Garbage(temp_x, temp_y))
-        world.vacuum = Vacuum(1, 1)
-        world.doc_station = Doc_Station(9, 8)
+
+        x, y = config.get("CONSTANT", "RobotStartPosition").split(",")
+        x, y = int(x), int(y)
+        world.vacuum = Vacuum(x, y)
+
+        x, y = config.get("CONSTANT", "DockStationStartPosition").split(",")
+        x, y = int(x), int(y)
+        world.doc_station = Doc_Station(x, y)
         if config.getboolean("APP", "cat"):
             world.cat = Cat(7, 8)
             world.add_entity(world.cat)
+
+        world.add_entity(world.doc_station)
+        world.add_entity(world.vacuum)
         world.add_entity(Entity(2, 8, "PLANT1"))
         world.add_entity(Entity(4, 1, "PLANT1"))
         world.add_entity(Entity(3, 4, "PLANT2"))
         world.add_entity(Entity(8, 8, "PLANT2"))
         world.add_entity(Entity(9, 3, "PLANT3"))
-        world.add_entity(Earring(9, 7))
-        world.add_entity(Earring(5, 5))
-        world.add_entity(Earring(4, 6))
+
+        numberOfEarrings = config.getint("CONSTANT", "NumberOfEarrings")
+        for _ in range(numberOfEarrings):
+            temp_x = randint(0, tiles_x - 1)
+            temp_y = randint(0, tiles_y - 1)
+
+            while world.is_entity_at(temp_x, temp_y):
+                temp_x = randint(0, tiles_x - 1)
+                temp_y = randint(0, tiles_y - 1)
+
+            world.add_entity(Earring(temp_x, temp_y))
+
+        for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
+            temp_x = randint(0, tiles_x - 1)
+            temp_y = randint(0, tiles_y - 1)
+
+            while world.is_entity_at(temp_x, temp_y):
+                temp_x = randint(0, tiles_x - 1)
+                temp_y = randint(0, tiles_y - 1)
+
+            world.add_entity(Garbage(temp_x, temp_y))
     else:
-        def world_adder(x,y,object,style=None):
+
+        def world_adder(x, y, object, style=None):
             print(object)
-            if object == 'Plant':
+            if object == "Plant":
                 world.add_entity(Entity(x, y, f"PLANT{randint(1, 3)}"))
-            if object == 'Earings':
+            if object == "Earings":
                 world.add_entity(Earring(x, y))
-            if object == 'Banana':
+            if object == "Banana":
                 world.add_entity(Garbage(temp_x, temp_y))
-            if object == 'Cat' and config.getboolean("APP", "cat"):
+            if object == "Cat" and config.getboolean("APP", "cat"):
                 world.add_entity(Cat(x, y))
 
         neural_network = VacuumRecognizer()
@@ -188,28 +230,27 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
         for _ in range(config.getint("CONSTANT", "NumberOfPlants")):
             temp_x = randint(0, tiles_x - 1)
             temp_y = randint(0, tiles_y - 1)
-            path = VacuumRecognizer.get_random_dir(neural_network,'Plant')
+            path = VacuumRecognizer.get_random_dir(neural_network, "Plant")
             world_adder(temp_x, temp_y, neural_network.recognize(path))
 
         for _ in range(config.getint("CONSTANT", "NumberOfEarrings")):
             temp_x = randint(0, tiles_x - 1)
             temp_y = randint(0, tiles_y - 1)
-            path = VacuumRecognizer.get_random_dir(neural_network,'Earings')
+            path = VacuumRecognizer.get_random_dir(neural_network, "Earings")
             world_adder(temp_x, temp_y, neural_network.recognize(path))
 
         for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
             temp_x = randint(0, tiles_x - 1)
             temp_y = randint(0, tiles_y - 1)
-            path = VacuumRecognizer.get_random_dir(neural_network,'Banana')
+            path = VacuumRecognizer.get_random_dir(neural_network, "Banana")
             world_adder(temp_x, temp_y, neural_network.recognize(path))
 
-
     for x in range(world.width):
         for y in range(world.height):
-            if world.is_garbage_at(x, y):
+            if world.garbage_at(x, y):
                 world.costs[x][y] = 1
             else:
-                world.costs[x][y] = 10
+                world.costs[x][y] = 2
     return world
 
 

view/renderer.py

@@ -94,7 +94,7 @@ class Renderer:
                     self.tile_height + self.tile_height / 4,
                 ),
             ),
-             "EARRING": pygame.transform.scale(
+            "EARRING": pygame.transform.scale(
                 pygame.image.load("media/sprites/earrings.webp"),
                 (
                     self.tile_width + self.tile_width / 4,
@@ -115,16 +115,8 @@ class Renderer:
         self.render_board()
         for x in range(world.width):
             for y in range(world.height):
-                for entity in world.dust[x][y]:
+                for entity in world.entity[x][y]:
                     self.draw_entity(entity)
-        for x in range(world.width):
-            for y in range(world.height):
-                for entity in world.obstacles[x][y]:
-                    self.draw_entity(entity)
-        self.draw_entity(world.vacuum)
-        self.draw_entity(world.doc_station)
-        if config.getboolean("APP", "cat"):
-            self.draw_entity(world.cat)
         pygame.display.update()
 
     def line(self, x_1, y_1, x_2, y_2, color=None):