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Merge pull request 'genetic_alg' (#34) from genetic_alg into master

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Reviewed-on: #34
This commit is contained in:
Angelika Iskra 2022-06-06 15:19:58 +02:00
commit 9afd1f366a
16 changed files with 517 additions and 39 deletions
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3
algorithms/a_star.py
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@ -4,10 +4,11 @@ import heapq
from dataclasses import dataclass, field
from typing import Tuple, Optional, List
from algorithms.genetic.const import MAP_ALIASES
from common.constants import ROWS, COLUMNS, LEFT, RIGHT, UP, DOWN
from common.helpers import directions
EMPTY_FIELDS = ['s', 'g', ' ']
EMPTY_FIELDS = [MAP_ALIASES.get("SAND"), MAP_ALIASES.get("GRASS"), ' ']
TURN_LEFT = 'TURN_LEFT'
TURN_RIGHT = 'TURN_RIGHT'

142
algorithms/genetic/common.py Normal file
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@ -0,0 +1,142 @@
from dataclasses import dataclass
import numpy as np
from const import *
from typing import List, Dict, Tuple
import numpy.typing as npt
@dataclass
class Position:
row: int
col: int
@dataclass
class Area:
position: Position
width: int
height: int
AREAS_TO_CROSS = [
# up above left knights spawn
Area(position=Position(row=0, col=0),
width=KNIGHTS_SPAWN_WIDTH,
height=LEFT_KNIGHTS_SPAWN_FIRST_ROW),
# down below left knights spawn
Area(position=Position(row=LEFT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=0),
width=KNIGHTS_SPAWN_WIDTH,
height=ROWS - LEFT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
# between left knights spawn and castle
Area(position=Position(row=0, col=KNIGHTS_SPAWN_WIDTH),
width=CASTLE_SPAWN_FIRST_COL - KNIGHTS_SPAWN_WIDTH,
height=ROWS),
# up above castle
Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL),
width=2,
height=CASTLE_SPAWN_FIRST_ROW),
# down below castle
Area(position=Position(row=CASTLE_SPAWN_FIRST_ROW + 2, col=CASTLE_SPAWN_FIRST_COL),
width=2,
height=ROWS - CASTLE_SPAWN_FIRST_ROW - 2),
# between castle and right knights spawn
Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL + 2),
width=RIGHT_KNIGHTS_SPAWN_FIRST_COL - CASTLE_SPAWN_FIRST_COL - 2,
height=ROWS),
# up above right knights spawn
Area(position=Position(row=0, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
width=KNIGHTS_SPAWN_WIDTH,
height=RIGHT_KNIGHTS_SPAWN_FIRST_ROW),
# down below right knights spawn
Area(position=Position(row=RIGHT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
width=KNIGHTS_SPAWN_WIDTH,
height=ROWS - RIGHT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
]
def dfs(grid: npt.NDArray, visited: Dict[Tuple[int, int], bool], position: Position, rows: int, cols: int) -> None:
visited[(position.row, position.col)] = True
row_vector = [0, 0, 1, -1]
col_vector = [-1, 1, 0, 0]
neighbours = []
for i in range(4):
rr = position.row + row_vector[i]
cc = position.col + col_vector[i]
if rr < 0 or rr >= ROWS:
continue
elif cc < 0 or cc >= COLUMNS:
continue
else:
p = Position(rr, cc)
if (p.row, p.col) in visited:
neighbours.append(p)
for neighbour in neighbours:
if not visited[(neighbour.row, neighbour.col)]:
dfs(grid, visited, neighbour, rows, cols)
def get_islands(grid: npt.NDArray, positions: List[Position], rows: int = ROWS, cols: int = COLUMNS) -> List[Position]:
"""it returns list of all islands roots"""
visited = {}
for position in positions:
visited[(position.row, position.col)] = False
islands = 0
roots = []
for position in positions:
if not visited[(position.row, position.col)]:
dfs(grid, visited, position, rows, cols)
roots.append(position)
islands += 1
return roots
def find_neighbours(grid: npt.NDArray, col: int, row: int) -> List[Position]:
dr = [-1, 1, 0, 0]
dc = [0, 0, -1, 1]
neighbours = []
for i in range(4):
rr = row + dr[i]
cc = col + dc[i]
if 0 <= rr < ROWS and 0 <= cc < COLUMNS and grid[rr][cc] == MAP_ALIASES.get('GRASS'):
neighbours.append(Position(row=rr, col=cc))
return neighbours
def get_tiles_positions(grid: npt.NDArray):
sands = []
trees = []
waters = []
monsters = []
for row_num in range(len(grid)):
for col_num in range(len(grid[row_num])):
if grid[row_num][col_num] == MAP_ALIASES.get('WATER'):
waters.append(Position(row=row_num, col=col_num))
elif grid[row_num][col_num] == MAP_ALIASES.get('TREE'):
trees.append(Position(row=row_num, col=col_num))
elif grid[row_num][col_num] == MAP_ALIASES.get('SAND'):
sands.append(Position(row=row_num, col=col_num))
elif grid[row_num][col_num] == MAP_ALIASES.get('MONSTER'):
monsters.append(Position(row=row_num, col=col_num))
return sands, trees, waters, monsters

29
algorithms/genetic/const.py Normal file
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@ -0,0 +1,29 @@
# map config
KNIGHTS_PER_TEAM_COUNT = 4
SAND_COUNT = 21
WATER_COUNT = 21
TREE_COUNT = 37
MONSTERS_COUNT = 2
CASTLES_COUNT = 1
ROWS = 19
COLUMNS = 24
KNIGHTS_SPAWN_WIDTH = 4
KNIGHTS_SPAWN_HEIGHT = 7
LEFT_KNIGHTS_SPAWN_FIRST_ROW = 6
LEFT_KNIGHTS_SPAWN_FIRST_COL = 0
RIGHT_KNIGHTS_SPAWN_FIRST_ROW = 6
RIGHT_KNIGHTS_SPAWN_FIRST_COL = 20
CASTLE_SPAWN_FIRST_ROW = 7
CASTLE_SPAWN_FIRST_COL = 11
# map aliases
MAP_ALIASES = {
"GRASS": 0,
"SAND": 1,
"WATER": 2,
"TREE": 3,
"MONSTER": 4,
"CASTLE": 5,
"KNIGHT_RED": 6,
"KNIGHT_BLUE": 7,
}

166
algorithms/genetic/genome.py Normal file
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@ -0,0 +1,166 @@
import math
import random
from copy import deepcopy
from random import randrange
from typing import List
import numpy as np
import numpy.typing as npt
from common import Position, get_islands, AREAS_TO_CROSS, find_neighbours, get_tiles_positions
from const import *
class Genome:
grid: npt.NDArray
knights_red: List[Position]
knights_blue: List[Position]
waters: List[Position]
trees: List[Position]
sands: List[Position]
monsters: List[Position]
fitness: int
sand_islands: List[Position]
tree_islands: List[Position]
water_islands: List[Position]
def __init__(self):
self.grid = np.zeros((ROWS, COLUMNS), dtype=int)
self.fitness = 0
self.knights_red = spawn_objects_in_given_area(
grid=self.grid,
object_alias=MAP_ALIASES.get("KNIGHT_RED"),
objects_count=KNIGHTS_PER_TEAM_COUNT,
spawn_position_start=Position(row=LEFT_KNIGHTS_SPAWN_FIRST_ROW, col=LEFT_KNIGHTS_SPAWN_FIRST_COL),
width=KNIGHTS_SPAWN_WIDTH,
height=KNIGHTS_SPAWN_HEIGHT
)
self.knights_blue = spawn_objects_in_given_area(
grid=self.grid,
object_alias=MAP_ALIASES.get("KNIGHT_BLUE"),
objects_count=KNIGHTS_PER_TEAM_COUNT,
spawn_position_start=Position(row=RIGHT_KNIGHTS_SPAWN_FIRST_ROW, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
width=KNIGHTS_SPAWN_WIDTH,
height=KNIGHTS_SPAWN_HEIGHT
)
spawn_objects_in_given_area(
grid=self.grid,
object_alias=MAP_ALIASES.get("CASTLE"),
objects_count=4,
spawn_position_start=Position(row=CASTLE_SPAWN_FIRST_ROW, col=CASTLE_SPAWN_FIRST_COL),
width=2,
height=2
)
self.waters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("WATER"),
objects_count=WATER_COUNT)
self.trees = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("TREE"),
objects_count=TREE_COUNT)
self.sands = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("SAND"),
objects_count=SAND_COUNT)
self.monsters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("MONSTER"),
objects_count=MONSTERS_COUNT)
self.sand_islands = get_islands(self.grid, self.sands)
self.tree_islands = get_islands(self.grid, self.trees)
self.water_islands = get_islands(self.grid, self.waters)
def update_map(self):
self.sands, self.trees, self.waters, self.monsters = get_tiles_positions(self.grid)
self.sand_islands = get_islands(self.grid, self.sands)
self.tree_islands = get_islands(self.grid, self.trees)
self.water_islands = get_islands(self.grid, self.waters)
def calc_fitness(self):
score = SAND_COUNT + TREE_COUNT + WATER_COUNT
score = score - len(self.sand_islands) - len(self.tree_islands) - len(self.water_islands)
sands, trees, waters, monsters = get_tiles_positions(self.grid)
if len(monsters) != MONSTERS_COUNT:
self.fitness = 0
return
if len(sands) < SAND_COUNT or len(trees) < TREE_COUNT or len(waters) < WATER_COUNT:
self.fitness = 5
return
self.fitness = score
def crossover(self, partner):
# replace a randomly selected part of the grid with partner's part
child = Genome()
child.grid = deepcopy(self.grid)
area_to_cross = random.choice(AREAS_TO_CROSS)
for row in range(area_to_cross.position.row, area_to_cross.position.row + area_to_cross.height):
for col in range(area_to_cross.position.col, area_to_cross.position.col + area_to_cross.width):
child.grid[row][col] = partner.grid[row][col]
child.update_map()
return child
def mutate(self, mutation_rate: float):
# remove 1 item from a random island and add a neighbor to another island
if random.random() < mutation_rate:
# select islands of the same, random type
islands_of_same_type = random.choice([self.sand_islands, self.tree_islands, self.water_islands])
random_index = random.randint(0, len(islands_of_same_type) - 1)
island = islands_of_same_type[random_index]
next_island = islands_of_same_type[(random_index + 1) % len(islands_of_same_type)]
free_tiles_nearby = find_neighbours(self.grid, next_island.col, next_island.row)
tile_type = self.grid[island.row][island.col]
self.grid[island.row][island.col] = MAP_ALIASES.get('GRASS')
# todo: if there are no free tiles around then randomize another next_island
if len(free_tiles_nearby) > 0:
random_free_tile = random.choice(free_tiles_nearby)
island.row = random_free_tile.row
island.col = random_free_tile.col
self.grid[island.row][island.col] = tile_type
self.update_map()
def is_empty(grid: npt.NDArray, position: Position) -> bool:
return grid[position.row, position.col] in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND")]
def is_invalid_area(spawn_position_start, height, width) -> bool:
return spawn_position_start.row + height - 1 < 0 or \
spawn_position_start.row + height - 1 >= ROWS or \
spawn_position_start.col + width - 1 < 0 or \
spawn_position_start.col + width - 1 >= COLUMNS
def spawn_objects_in_given_area(grid: npt.NDArray,
object_alias: str,
objects_count: int = 1,
spawn_position_start: Position = Position(row=0, col=0),
width: int = COLUMNS,
height: int = ROWS) -> List[Position]:
if is_invalid_area(spawn_position_start, height, width):
raise ValueError("Invalid spawn area")
objects_remaining = int(objects_count)
positions = []
while objects_remaining > 0:
row = randrange(spawn_position_start.row, spawn_position_start.row + height)
col = randrange(spawn_position_start.col, spawn_position_start.col + width)
position = Position(row=row, col=col)
if is_empty(grid=grid, position=position):
grid[position.row, position.col] = object_alias
positions.append(position)
objects_remaining -= 1
return positions

26
algorithms/genetic/map_generator.py Normal file
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@ -0,0 +1,26 @@
from algorithms.genetic.genome import Genome
from algorithms.genetic.map_importer_exporter import export_map
from population import Population
def main() -> None:
population_size = 500
mutation_rate = 0.3
population = Population(mutation_rate, population_size, 55)
while not population.evaluate():
# create next generation
population.generate()
# calc fitness
population.calc_fitness()
print(population.best_genome.grid)
print("Fitness of the best: ", population.best_genome.fitness)
export_map(population.best_genome.grid)
if __name__ == '__main__':
main()

42
algorithms/genetic/map_importer_exporter.py Normal file
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@ -0,0 +1,42 @@
import json
import random
import string
from datetime import datetime
from pathlib import Path
import numpy
import numpy.typing as npt
from os import listdir
from os.path import isfile, join
# Save map to file
def export_map(grid: npt.NDArray):
json_data = {"map": grid.tolist()}
now = datetime.now()
file_name = "map_" + now.strftime("%Y_%m_%d_%H_%M_%S") + ".json"
path = Path("../../resources/maps/")
file_to_open = path / file_name
with open(file_to_open, "w+") as write_file:
json.dump(json_data, write_file)
print("Saved map to file " + file_name)
def import_random_map() -> object:
path = "resources/maps"
files = [f for f in listdir(path) if isfile(join(path, f))]
random_map_name = random.choice(files)
return import_map(random_map_name)
# Read map from file
def import_map(file_name: string) -> object:
file_to_open = "resources/maps/" + file_name
with open(file_to_open, "r") as read_file:
print("Reading map from file " + file_name)
decoded_json = json.load(read_file)
decoded_grid = numpy.asarray(decoded_json["map"])
print(decoded_grid)
return decoded_grid.tolist()

81
algorithms/genetic/population.py Normal file
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@ -0,0 +1,81 @@
import random
from typing import List
import numpy as np
import numpy.typing as npt
from genome import Genome
class Population:
population: List[Genome] = [] # array to hold the current population
mating_pool: List[Genome] = [] # array which we will use for our "mating pool"
generations: int = 0 # number of generations
finished: bool = False # are we finished evolving?
mutation_rate: float
perfect_score: int
best_genome: Genome
def __init__(self, mutation_rate, population_size, perfect_score=20):
self.mutation_rate = mutation_rate
self.perfect_score = perfect_score
for i in range(0, population_size):
new_genome = Genome()
new_genome.calc_fitness()
self.population.append(new_genome)
# create a new generation
def generate(self):
max_fitness = 0
for genome in self.population:
if genome.fitness > max_fitness:
max_fitness = genome.fitness
print("Max fitness of generation " + str(self.generations) + " = " + str(max_fitness))
# refill the population with children from the mating pool
new_population = []
for genome in self.population:
partner_a = self.accept_reject(max_fitness)
partner_b = self.accept_reject(max_fitness)
child = partner_a.crossover(partner_b)
child.mutate(self.mutation_rate)
new_population.append(child)
self.population = new_population
self.generations += 1
# select random with correct probability from population
def accept_reject(self, max_fitness: int):
safe_flag = 0
while safe_flag < 10000:
partner = random.choice(self.population)
r = random.randint(0, max_fitness)
if r < partner.fitness:
return partner
safe_flag += 1
# compute the current "most fit" member of the population
def evaluate(self):
record = 0
best_index = 0
for index in range(len(self.population)):
genome = self.population[index]
if genome.fitness > record:
record = genome.fitness
best_index = index
self.best_genome = self.population[best_index]
if record >= self.perfect_score:
self.finished = True
return self.finished
def calc_fitness(self):
for genome in self.population:
genome.calc_fitness()

0
algorithms/neural_network/__init__.py Normal file
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2
common/constants.py
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@ -6,7 +6,7 @@ GAME_TITLE = 'WMICraft'
WINDOW_HEIGHT = 800
WINDOW_WIDTH = 1360
FPS_COUNT = 60
TURN_INTERVAL = 300
TURN_INTERVAL = 500
GRID_CELL_PADDING = 5
GRID_CELL_SIZE = 36

6
common/helpers.py
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@ -1,6 +1,8 @@
from typing import Tuple, List
import pygame
from algorithms.genetic.const import MAP_ALIASES
from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS, CLASSES, CLASS_TO_ID
import csv
import os
@ -99,7 +101,7 @@ def castle_neighbors(map, castle_bottom_right_row, castle_bottom_right_col):
return neighbors
def find_neighbours(grid: List[List[str]], col: int, row: int) -> List[Tuple[int, int]]:
def find_neighbours(grid: List[List[int]], col: int, row: int) -> List[Tuple[int, int]]:
dr = [-1, 1, 0, 0]
dc = [0, 0, -1, 1]
@ -111,7 +113,7 @@ def find_neighbours(grid: List[List[str]], col: int, row: int) -> List[Tuple[int
if rr < 0 or cc < 0: continue
if rr >= ROWS or cc >= COLUMNS: continue
if grid[rr][cc] not in ['g', 's', '.']: continue
if grid[rr][cc] not in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND"), '.']: continue
neighbours.append((rr, cc))
return neighbours

11
learning/decision_tree.py
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@ -34,7 +34,7 @@ class DecisionTree:
self.model = DecisionTreeClassifier(criterion='entropy')
self.model.fit(self.train_set.values, self.goals)
def predict_move(self, grid: List[List[str]], current_knight: Knight, castle: Castle, monsters: List[Monster],
def predict_move(self, grid: List[List[int]], current_knight: Knight, castle: Castle, monsters: List[Monster],
opponents: List[Knight]) -> \
List[Tuple[int, int]]:
distance_to_castle = manhattan_distance(current_knight.position, castle.position)
@ -42,14 +42,15 @@ class DecisionTree:
monsters_parsed = []
for monster in monsters:
monsters_parsed.append((manhattan_distance(current_knight.position, monster.position), parse_hp(
monster.current_hp)))
monster.health_bar.current_hp)))
opponents_parsed = []
for opponent in opponents:
opponents_parsed.append(
(manhattan_distance(current_knight.position, opponent.position), parse_hp(opponent.health_bar.current_hp)))
(manhattan_distance(current_knight.position, opponent.position),
parse_hp(opponent.health_bar.current_hp)))
prediction = self.get_prediction(tower_dist=distance_to_castle, tower_hp=castle.current_hp,
prediction = self.get_prediction(tower_dist=distance_to_castle, tower_hp=castle.health_bar.current_hp,
mob1_dist=monsters_parsed[0][0], mob1_hp=monsters_parsed[0][1],
mob2_dist=monsters_parsed[1][0], mob2_hp=monsters_parsed[1][1],
opp1_dist=opponents_parsed[0][0], opp1_hp=opponents_parsed[0][1],
@ -57,7 +58,7 @@ class DecisionTree:
opp3_dist=opponents_parsed[2][0], opp3_hp=opponents_parsed[2][1],
opp4_dist=opponents_parsed[3][0], opp4_hp=opponents_parsed[3][1],
agent_hp=current_knight.health_bar.current_hp)
print(prediction)
print(f'Prediction = {prediction}')
if prediction == 'tower': # castle...
return castle_neighbors(grid, castle_bottom_right_row=castle.position[0],
castle_bottom_right_col=castle.position[1])

45
logic/level.py
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@ -3,10 +3,11 @@ import random
import pygame
from algorithms.a_star import a_star, State, TURN_RIGHT, TURN_LEFT, FORWARD
from algorithms.genetic.const import MAP_ALIASES
from algorithms.genetic.map_importer_exporter import import_random_map
from common.constants import *
from learning.decision_tree import DecisionTree
from logic.knights_queue import KnightsQueue
from logic.spawner import Spawner
from models.castle import Castle
from models.knight import Knight
from models.monster import Monster
@ -21,7 +22,7 @@ class Level:
# sprite group setup
self.sprites = pygame.sprite.LayeredUpdates()
self.map = [['g' for _ in range(COLUMNS)] for y in range(ROWS)]
self.map = []
self.list_knights_blue = []
self.list_knights_red = []
@ -31,27 +32,11 @@ class Level:
self.knights_queue = None
def create_map(self):
self.generate_map()
self.map = import_random_map()
self.setup_base_tiles()
self.setup_objects()
self.knights_queue = KnightsQueue(self.list_knights_blue, self.list_knights_red)
def generate_map(self):
spawner = Spawner(self.map)
spawner.spawn_where_possible(['w' for _ in range(NBR_OF_WATER)])
spawner.spawn_where_possible(['t' for _ in range(NBR_OF_TREES)])
spawner.spawn_where_possible(['s' for _ in range(NBR_OF_SANDS)])
spawner.spawn_in_area(['k_b' for _ in range(4)], LEFT_KNIGHTS_SPAWN_FIRST_ROW, LEFT_KNIGHTS_SPAWN_FIRST_COL,
KNIGHTS_SPAWN_WIDTH, KNIGHTS_SPAWN_HEIGHT)
spawner.spawn_in_area(['k_r' for _ in range(4)], RIGHT_KNIGHTS_SPAWN_FIRST_ROW, RIGHT_KNIGHTS_SPAWN_FIRST_COL,
KNIGHTS_SPAWN_WIDTH, KNIGHTS_SPAWN_HEIGHT)
spawner.spawn_in_area(['c'], CASTLE_SPAWN_FIRST_ROW, CASTLE_SPAWN_FIRST_COL, CASTLE_SPAWN_WIDTH,
CASTLE_SPAWN_HEIGHT, 2)
spawner.spawn_where_possible(['m' for _ in range(NBR_OF_MONSTERS)])
def setup_base_tiles(self):
textures = []
for texture_path in TILES:
@ -63,15 +48,15 @@ class Level:
for col_index, col in enumerate(row):
# add base tiles, e.g. water, tree, grass
if col == "w":
if col == MAP_ALIASES.get('WATER'):
texture_index = 5
texture_surface = textures[texture_index][1]
Tile((col_index, row_index), texture_surface, self.sprites, 'w')
elif col == "t":
elif col == MAP_ALIASES.get('TREE'):
texture_index = 6
texture_surface = textures[texture_index][1]
Tile((col_index, row_index), texture_surface, self.sprites, 't')
elif col == "s":
elif col == MAP_ALIASES.get('SAND'):
texture_index = 4
texture_surface = textures[texture_index][1]
Tile((col_index, row_index), texture_surface, self.sprites)
@ -88,19 +73,19 @@ class Level:
for col_index, col in enumerate(row):
# add objects, e.g. knights, monsters, castle
if col == "k_b":
if col == MAP_ALIASES.get('KNIGHT_BLUE'):
knight = Knight(self.screen, (col_index, row_index), self.sprites, "blue")
self.map[row_index][col_index] = knight
self.list_knights_blue.append(knight)
elif col == "k_r":
elif col == MAP_ALIASES.get('KNIGHT_RED'):
knight = Knight(self.screen, (col_index, row_index), self.sprites, "red")
self.map[row_index][col_index] = knight
self.list_knights_red.append(knight)
elif col == "m":
elif col == MAP_ALIASES.get('MONSTER'):
monster = Monster(self.screen, (col_index, row_index), self.sprites)
self.map[row_index][col_index] = monster
self.list_monsters.append(monster)
elif col == "c":
elif col == MAP_ALIASES.get('CASTLE'):
castle_count += 1
if castle_count == 4:
castle = Castle(self.screen, (col_index, row_index), self.sprites)
@ -108,15 +93,15 @@ class Level:
self.list_castles.append(castle)
def handle_turn(self):
print("next turn")
current_knight = self.knights_queue.dequeue_knight()
print("next turn " + current_knight.team)
knight_pos_x = current_knight.position[0]
knight_pos_y = current_knight.position[1]
goal_list = self.decision_tree.predict_move(grid=self.map, current_knight=current_knight,
monsters=self.list_monsters,
opponents=self.list_knights_red
if current_knight.team_alias == 'k_r' else self.list_knights_blue,
opponents=self.list_knights_blue
if current_knight.team_alias() == 'k_r' else self.list_knights_red,
castle=self.list_castles[0])
if len(goal_list) == 0:
@ -139,7 +124,7 @@ class Level:
current_knight.rotate_right()
elif next_action == FORWARD:
current_knight.step_forward()
self.map[knight_pos_y][knight_pos_x] = 'g'
self.map[knight_pos_y][knight_pos_x] = MAP_ALIASES.get("GRASS")
# update knight on map
if current_knight.direction.name == UP:

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resources/maps/map_2022_06_02_13_27_18.json Normal file
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@ -0,0 +1 @@
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resources/maps/map_2022_06_06_14_55_49.json Normal file
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@ -0,0 +1 @@
{"map": [[0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 0, 0, 0, 2, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 3, 3, 0, 0, 0, 0, 0, 3, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 7, 7, 0, 0], [6, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 3, 7, 0, 0, 0], [0, 0, 6, 6, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 0, 0, 0, 0, 0, 4, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}

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resources/maps/map_2022_06_06_14_58_11.json Normal file
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@ -0,0 +1 @@
{"map": [[0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 3], [0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 3, 0], [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 2, 0], [0, 0, 3, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0], [0, 0, 3, 0, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 3, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 6, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 6, 0, 0, 0, 0, 3, 0, 0, 0, 0, 5, 5, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 5, 5, 1, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 4], [6, 0, 0, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 1, 0, 7, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 2, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0], [0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}