Предыдущая статья — Python AI в StarCraft II. Часть XI: обучение нейронной сети.
В двенадцатой части серии статей про использование искусственного интеллекта в игре Starcraft II мы рассмотрим код для эффективного тестирования нашей модели в реальной игре и обсудим некоторые интересные результаты.
Если вы еще не создали свою собственную модель, то можете скачать нашу.
Для начала мы хотим, чтобы наш AI можно было легко отличить от других. Для этого добавим в наш метод __init__ дескриптор:
def __init__(self, use_model=False):
self.ITERATIONS_PER_MINUTE = 165
self.MAX_WORKERS = 50
self.do_something_after = 0
self.use_model = use_model
self.train_data = []
if self.use_model:
print("USING MODEL!")
self.model = keras.models.load_model("BasicCNN-30-epochs-0.0001-LR-4.2")
Для сравнения моделей мы будем логировать результаты игр:
def on_end(self, game_result):
print('--- on_end called ---')
print(game_result, self.use_model)
with open("log.txt","a") as f:
if self.use_model:
f.write("Model {}\n".format(game_result))
else:
f.write("Random {}\n".format(game_result))
Наконец, в методе attack, если флаг use_model равен True, мы будем использовать нашу модель, а в противном случае выберем вариант атаки случайным образом:
async def attack(self):
if len(self.units(VOIDRAY).idle) > 0:
target = False
if self.iteration > self.do_something_after:
if self.use_model:
prediction = self.model.predict([self.flipped.reshape([-1, 176, 200, 3])])
choice = np.argmax(prediction[0])
#print('prediction: ',choice)
choice_dict = {0: "No Attack!",
1: "Attack close to our nexus!",
2: "Attack Enemy Structure!",
3: "Attack Eneemy Start!"}
print("Choice #{}:{}".format(choice, choice_dict[choice]))
else:
choice = random.randrange(0, 4)
Затем выполним следующий код для запуска игры:
for i in range(100):
run_game(maps.get("AbyssalReefLE"), [
Bot(Race.Protoss, SentdeBot(use_model=True)),
Computer(Race.Protoss, Difficulty.Medium),
], realtime=False)
Полный код выглядит вот так:
import sc2
from sc2 import run_game, maps, Race, Difficulty, Result
from sc2.player import Bot, Computer
from sc2 import position
from sc2.constants import NEXUS, PROBE, PYLON, ASSIMILATOR, GATEWAY, \
CYBERNETICSCORE, STARGATE, VOIDRAY, SCV, DRONE, ROBOTICSFACILITY, OBSERVER
import random
import cv2
import numpy as np
import os
import time
import keras
#os.environ["SC2PATH"] = '/starcraftstuff/StarCraftII/'
HEADLESS = False
class SentdeBot(sc2.BotAI):
def __init__(self, use_model=False):
self.ITERATIONS_PER_MINUTE = 165
self.MAX_WORKERS = 50
self.do_something_after = 0
self.use_model = use_model
self.train_data = []
#####
if self.use_model:
print("USING MODEL!")
self.model = keras.models.load_model("BasicCNN-30-epochs-0.0001-LR-4.2")
def on_end(self, game_result):
print('--- on_end called ---')
print(game_result, self.use_model)
with open("gameout-random-vs-medium.txt","a") as f:
if self.use_model:
f.write("Model {}\n".format(game_result))
else:
f.write("Random {}\n".format(game_result))
async def on_step(self, iteration):
self.iteration = iteration
await self.scout()
await self.distribute_workers()
await self.build_workers()
await self.build_pylons()
await self.build_assimilators()
await self.expand()
await self.offensive_force_buildings()
await self.build_offensive_force()
await self.intel()
await self.attack()
def random_location_variance(self, enemy_start_location):
x = enemy_start_location[0]
y = enemy_start_location[1]
# FIXED THIS
x += ((random.randrange(-20, 20))/100) * self.game_info.map_size[0]
y += ((random.randrange(-20, 20))/100) * self.game_info.map_size[1]
if x < 0:
print("x below")
x = 0
if y < 0:
print("y below")
y = 0
if x > self.game_info.map_size[0]:
print("x above")
x = self.game_info.map_size[0]
if y > self.game_info.map_size[1]:
print("y above")
y = self.game_info.map_size[1]
go_to = position.Point2(position.Pointlike((x,y)))
return go_to
async def scout(self):
'''
['__call__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_game_data', '_proto', '_type_data', 'add_on_tag', 'alliance', 'assigned_harvesters', 'attack', 'build', 'build_progress', 'cloak', 'detect_range', 'distance_to', 'energy', 'facing', 'gather', 'has_add_on', 'has_buff', 'health', 'health_max', 'hold_position', 'ideal_harvesters', 'is_blip', 'is_burrowed', 'is_enemy', 'is_flying', 'is_idle', 'is_mine', 'is_mineral_field', 'is_powered', 'is_ready', 'is_selected', 'is_snapshot', 'is_structure', 'is_vespene_geyser', 'is_visible', 'mineral_contents', 'move', 'name', 'noqueue', 'orders', 'owner_id', 'position', 'radar_range', 'radius', 'return_resource', 'shield', 'shield_max', 'stop', 'tag', 'train', 'type_id', 'vespene_contents', 'warp_in']
'''
if len(self.units(OBSERVER)) > 0:
scout = self.units(OBSERVER)[0]
if scout.is_idle:
enemy_location = self.enemy_start_locations[0]
move_to = self.random_location_variance(enemy_location)
print(move_to)
await self.do(scout.move(move_to))
else:
for rf in self.units(ROBOTICSFACILITY).ready.noqueue:
if self.can_afford(OBSERVER) and self.supply_left > 0:
await self.do(rf.train(OBSERVER))
async def intel(self):
# for game_info: https://github.com/Dentosal/python-sc2/blob/master/sc2/game_info.py#L162
#print(self.game_info.map_size)
# flip around. It's y, x when you're dealing with an array.
game_data = np.zeros((self.game_info.map_size[1], self.game_info.map_size[0], 3), np.uint8)
# UNIT: [SIZE, (BGR COLOR)]
'''from sc2.constants import NEXUS, PROBE, PYLON, ASSIMILATOR, GATEWAY, \
CYBERNETICSCORE, STARGATE, VOIDRAY'''
draw_dict = {
NEXUS: [15, (0, 255, 0)],
PYLON: [3, (20, 235, 0)],
PROBE: [1, (55, 200, 0)],
ASSIMILATOR: [2, (55, 200, 0)],
GATEWAY: [3, (200, 100, 0)],
CYBERNETICSCORE: [3, (150, 150, 0)],
STARGATE: [5, (255, 0, 0)],
ROBOTICSFACILITY: [5, (215, 155, 0)],
#VOIDRAY: [3, (255, 100, 0)],
}
for unit_type in draw_dict:
for unit in self.units(unit_type).ready:
pos = unit.position
cv2.circle(game_data, (int(pos[0]), int(pos[1])), draw_dict[unit_type][0], draw_dict[unit_type][1], -1)
# NOT THE MOST IDEAL, BUT WHATEVER LOL
main_base_names = ["nexus", "commandcenter", "hatchery"]
for enemy_building in self.known_enemy_structures:
pos = enemy_building.position
if enemy_building.name.lower() not in main_base_names:
cv2.circle(game_data, (int(pos[0]), int(pos[1])), 5, (200, 50, 212), -1)
for enemy_building in self.known_enemy_structures:
pos = enemy_building.position
if enemy_building.name.lower() in main_base_names:
cv2.circle(game_data, (int(pos[0]), int(pos[1])), 15, (0, 0, 255), -1)
for enemy_unit in self.known_enemy_units:
if not enemy_unit.is_structure:
worker_names = ["probe",
"scv",
"drone"]
# if that unit is a PROBE, SCV, or DRONE... it's a worker
pos = enemy_unit.position
if enemy_unit.name.lower() in worker_names:
cv2.circle(game_data, (int(pos[0]), int(pos[1])), 1, (55, 0, 155), -1)
else:
cv2.circle(game_data, (int(pos[0]), int(pos[1])), 3, (50, 0, 215), -1)
for obs in self.units(OBSERVER).ready:
pos = obs.position
cv2.circle(game_data, (int(pos[0]), int(pos[1])), 1, (255, 255, 255), -1)
for vr in self.units(VOIDRAY).ready:
pos = vr.position
cv2.circle(game_data, (int(pos[0]), int(pos[1])), 3, (255, 100, 0), -1)
line_max = 50
mineral_ratio = self.minerals / 1500
if mineral_ratio > 1.0:
mineral_ratio = 1.0
vespene_ratio = self.vespene / 1500
if vespene_ratio > 1.0:
vespene_ratio = 1.0
population_ratio = self.supply_left / self.supply_cap
if population_ratio > 1.0:
population_ratio = 1.0
plausible_supply = self.supply_cap / 200.0
military_weight = len(self.units(VOIDRAY)) / (self.supply_cap-self.supply_left)
if military_weight > 1.0:
military_weight = 1.0
cv2.line(game_data, (0, 19), (int(line_max*military_weight), 19), (250, 250, 200), 3) # worker/supply ratio
cv2.line(game_data, (0, 15), (int(line_max*plausible_supply), 15), (220, 200, 200), 3) # plausible supply (supply/200.0)
cv2.line(game_data, (0, 11), (int(line_max*population_ratio), 11), (150, 150, 150), 3) # population ratio (supply_left/supply)
cv2.line(game_data, (0, 7), (int(line_max*vespene_ratio), 7), (210, 200, 0), 3) # gas / 1500
cv2.line(game_data, (0, 3), (int(line_max*mineral_ratio), 3), (0, 255, 25), 3) # minerals minerals/1500
# flip horizontally to make our final fix in visual representation:
self.flipped = cv2.flip(game_data, 0)
resized = cv2.resize(self.flipped, dsize=None, fx=2, fy=2)
if not HEADLESS:
if self.use_model:
cv2.imshow('Model Intel', resized)
cv2.waitKey(1)
else:
cv2.imshow('Random Intel', resized)
cv2.waitKey(1)
async def build_workers(self):
if (len(self.units(NEXUS)) * 16) > len(self.units(PROBE)) and len(self.units(PROBE)) < self.MAX_WORKERS:
for nexus in self.units(NEXUS).ready.noqueue:
if self.can_afford(PROBE):
await self.do(nexus.train(PROBE))
async def build_pylons(self):
if self.supply_left < 5 and not self.already_pending(PYLON):
nexuses = self.units(NEXUS).ready
if nexuses.exists:
if self.can_afford(PYLON):
await self.build(PYLON, near=nexuses.first)
async def build_assimilators(self):
for nexus in self.units(NEXUS).ready:
vaspenes = self.state.vespene_geyser.closer_than(15.0, nexus)
for vaspene in vaspenes:
if not self.can_afford(ASSIMILATOR):
break
worker = self.select_build_worker(vaspene.position)
if worker is None:
break
if not self.units(ASSIMILATOR).closer_than(1.0, vaspene).exists:
await self.do(worker.build(ASSIMILATOR, vaspene))
async def expand(self):
try:
if self.units(NEXUS).amount < (self.iteration / self.ITERATIONS_PER_MINUTE)/2 and self.can_afford(NEXUS):
await self.expand_now()
except Exception as e:
print(str(e))
async def offensive_force_buildings(self):
if self.units(PYLON).ready.exists:
pylon = self.units(PYLON).ready.random
if self.units(GATEWAY).ready.exists and not self.units(CYBERNETICSCORE):
if self.can_afford(CYBERNETICSCORE) and not self.already_pending(CYBERNETICSCORE):
await self.build(CYBERNETICSCORE, near=pylon)
elif len(self.units(GATEWAY)) < 1:
if self.can_afford(GATEWAY) and not self.already_pending(GATEWAY):
await self.build(GATEWAY, near=pylon)
if self.units(CYBERNETICSCORE).ready.exists:
if len(self.units(ROBOTICSFACILITY)) < 1:
if self.can_afford(ROBOTICSFACILITY) and not self.already_pending(ROBOTICSFACILITY):
await self.build(ROBOTICSFACILITY, near=pylon)
if self.units(CYBERNETICSCORE).ready.exists:
if len(self.units(STARGATE)) < (self.iteration / self.ITERATIONS_PER_MINUTE):
if self.can_afford(STARGATE) and not self.already_pending(STARGATE):
await self.build(STARGATE, near=pylon)
async def build_offensive_force(self):
for sg in self.units(STARGATE).ready.noqueue:
if self.can_afford(VOIDRAY) and self.supply_left > 0:
await self.do(sg.train(VOIDRAY))
def find_target(self, state):
if len(self.known_enemy_units) > 0:
return random.choice(self.known_enemy_units)
elif len(self.known_enemy_structures) > 0:
return random.choice(self.known_enemy_structures)
else:
return self.enemy_start_locations[0]
async def attack(self):
if len(self.units(VOIDRAY).idle) > 0:
target = False
if self.iteration > self.do_something_after:
if self.use_model:
prediction = self.model.predict([self.flipped.reshape([-1, 176, 200, 3])])
choice = np.argmax(prediction[0])
#print('prediction: ',choice)
choice_dict = {0: "No Attack!",
1: "Attack close to our nexus!",
2: "Attack Enemy Structure!",
3: "Attack Eneemy Start!"}
print("Choice #{}:{}".format(choice, choice_dict[choice]))
else:
choice = random.randrange(0, 4)
if choice == 0:
# no attack
wait = random.randrange(20,165)
self.do_something_after = self.iteration + wait
elif choice == 1:
#attack_unit_closest_nexus
if len(self.known_enemy_units) > 0:
target = self.known_enemy_units.closest_to(random.choice(self.units(NEXUS)))
elif choice == 2:
#attack enemy structures
if len(self.known_enemy_structures) > 0:
target = random.choice(self.known_enemy_structures)
elif choice == 3:
#attack_enemy_start
target = self.enemy_start_locations[0]
if target:
for vr in self.units(VOIDRAY).idle:
await self.do(vr.attack(target))
y = np.zeros(4)
y[choice] = 1
#print(y)
self.train_data.append([y,self.flipped])
#print(len(self.train_data))
for i in range(100):
run_game(maps.get("AbyssalReefLE"), [
Bot(Race.Protoss, SentdeBot(use_model=True)),
Computer(Race.Protoss, Difficulty.Medium),
], realtime=False)
В процессе тестирования (100 игр против среднего AI) мы выяснили, что случайная модель имеет 44% на успех, а наша нейронная сеть — 66%.
Отлично, ну и что дальше? Настало время вносить исправления и повышать сложность. Глубокое обучение определенно дает результаты, и мы можем их повышать. Но у нас осталось множество вещей, которые мы можем улучшить.
Например, можно лучше отслеживать время, усилить разведку, а также ряд других моментов. Кроме того, есть гораздо больше вариантов игры, которые наша нейронная сеть могла бы контролировать. В следующей статье мы этим и займемся.
Следующая статья — Python AI в StarCraft II. Часть XIII: улучшенная версия.
