Python AI в StarCraft II. Часть XVI: изменение визуализации

Предыдущая статья — Python AI в StarCraft II. Часть XV: увеличиваем вариативность.

В этой части серии статей про использование AI в игре Starcraft II мы собираемся изменить некоторые визуальные эффекты, которые обрабатывает наша нейронная сеть. Вероятно, нам не нужны цвета и мы хотим отображать больше юнитов.

За исключением строк, описывающих различные ресурсы, мы собираемся полностью переписать наш метод intel. И еще: вместо отслеживания весов военных юнитов мы будем отслеживать характеристики рабочих.

Начнем, как и раньше:

    async def intel(self):

        game_data = np.zeros((self.game_info.map_size[1], self.game_info.map_size[0], 3), np.uint8)

Теперь переберем наши текущие юниты:

        for unit in self.units().ready:
            pos = unit.position
            cv2.circle(game_data, (int(pos[0]), int(pos[1])), int(unit.radius*8), (255, 255, 255), math.ceil(int(unit.radius*0.5)))

Затем юниты врага:

 for unit in self.known_enemy_units:
            pos = unit.position
            cv2.circle(game_data, (int(pos[0]), int(pos[1])), int(unit.radius*8), (125, 125, 125), math.ceil(int(unit.radius*0.5)))

Обратите внимание, что сейчас мы просто рисуем круги. В качестве радиуса мы берем размер юнита. Наши юниты белые, а у противника серые.

А теперь нарисуем линии:

        try:
            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

            worker_weight = len(self.units(PROBE)) / (self.supply_cap-self.supply_left)
            if worker_weight > 1.0:
                worker_weight = 1.0

            cv2.line(game_data, (0, 19), (int(line_max*worker_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
        except Exception as e:
            print(str(e))

Обратите внимание, что мы заменили переменную military_weigh на worker_weigh, и соответственно, теперь отображаем размер нашего юнита рабочих.

А закончим мы этот метод вот так:

        # flip horizontally to make our final fix in visual representation:
        grayed = cv2.cvtColor(game_data, cv2.COLOR_BGR2GRAY)
        self.flipped = cv2.flip(grayed, 0)
        resized = cv2.resize(self.flipped, dsize=None, fx=2, fy=2)
        if not HEADLESS:
            cv2.imshow(str(self.title), resized)
            cv2.waitKey(1)

Теперь наше визуальное представление имеет примерно такой вид:

new basic visuals
machine learning logo

Английский для программистов

Наш телеграм канал с тестами по английскому языку для программистов. Английский это часть карьеры программиста. Поэтому полезно заняться им уже сейчас

×

Наконец, мы немного поиграли с весами случайных результатов, чтобы усилить нашу базу результатов для обучения.

Внутри нашего метода do_something мы поработаем вот с этими весами:

    async def do_something(self):

        if self.time > self.do_something_after:
            if self.use_model:
                prediction = self.model.predict([self.flipped.reshape([-1, 176, 200, 3])])
                choice = np.argmax(prediction[0])
            else:
                worker_weight = 8
                zealot_weight = 3
                voidray_weight = 20
                stalker_weight = 8
                pylon_weight = 5
                stargate_weight = 5
                gateway_weight = 3

                choice_weights = 1*[0]+zealot_weight*[1]+gateway_weight*[2]+voidray_weight*[3]+stalker_weight*[4]+worker_weight*[5]+1*[6]+stargate_weight*[7]+pylon_weight*[8]+1*[9]+1*[10]+1*[11]+1*[12]+1*[13]
                choice = random.choice(choice_weights)
            try:
                await self.choices[choice]()
            except Exception as e:
                print(str(e))

            y = np.zeros(14)
            y[choice] = 1
            self.train_data.append([y, self.flipped])

Вот полный код для создания обучающих данных:

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, \
 ZEALOT, STALKER
import random
import cv2
import numpy as np
import os
import time
import math

os.environ["SC2PATH"] = '/starcraftstuff/StarCraftII/'
HEADLESS = True

class SentdeBot(sc2.BotAI):
    def __init__(self, use_model=False, title=1):
        self.MAX_WORKERS = 50
        self.do_something_after = 0
        self.use_model = use_model
        self.title = title
        # DICT {UNIT_ID:LOCATION}
        # every iteration, make sure that unit id still exists!
        self.scouts_and_spots = {}

        # ADDED THE CHOICES #
        self.choices = {0: self.build_scout,
                        1: self.build_zealot,
                        2: self.build_gateway,
                        3: self.build_voidray,
                        4: self.build_stalker,
                        5: self.build_worker,
                        6: self.build_assimilator,
                        7: self.build_stargate,
                        8: self.build_pylon,
                        9: self.defend_nexus,
                        10: self.attack_known_enemy_unit,
                        11: self.attack_known_enemy_structure,
                        12: self.expand,  # might just be self.expand_now() lol
                        13: self.do_nothing,
                        }

        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)
        #if self.time < 17:
        if game_result == Result.Victory:
            np.save("train_data/{}.npy".format(str(int(time.time()))), np.array(self.train_data))

    async def on_step(self, iteration):

        self.time = (self.state.game_loop/22.4) / 60
        print('Time:',self.time)

        if iteration % 5 == 0:
            await self.distribute_workers()
        await self.scout()
        await self.intel()
        await self.do_something()

    def random_location_variance(self, location):
        x = location[0]
        y = location[1]

        #  FIXED THIS
        x += random.randrange(-5,5)
        y += random.randrange(-5,5)

        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):
        self.expand_dis_dir = {}

        for el in self.expansion_locations:
            distance_to_enemy_start = el.distance_to(self.enemy_start_locations[0])
            #print(distance_to_enemy_start)
            self.expand_dis_dir[distance_to_enemy_start] = el

        self.ordered_exp_distances = sorted(k for k in self.expand_dis_dir)

        existing_ids = [unit.tag for unit in self.units]
        # removing of scouts that are actually dead now.
        to_be_removed = []
        for noted_scout in self.scouts_and_spots:
            if noted_scout not in existing_ids:
                to_be_removed.append(noted_scout)

        for scout in to_be_removed:
            del self.scouts_and_spots[scout]

        if len(self.units(ROBOTICSFACILITY).ready) == 0:
            unit_type = PROBE
            unit_limit = 1
        else:
            unit_type = OBSERVER
            unit_limit = 15

        assign_scout = True

        if unit_type == PROBE:
            for unit in self.units(PROBE):
                if unit.tag in self.scouts_and_spots:
                    assign_scout = False

        if assign_scout:
            if len(self.units(unit_type).idle) > 0:
                for obs in self.units(unit_type).idle[:unit_limit]:
                    if obs.tag not in self.scouts_and_spots:
                        for dist in self.ordered_exp_distances:
                            try:
                                location = next(value for key, value in self.expand_dis_dir.items() if key == dist)
                                # DICT {UNIT_ID:LOCATION}
                                active_locations = [self.scouts_and_spots[k] for k in self.scouts_and_spots]

                                if location not in active_locations:
                                    if unit_type == PROBE:
                                        for unit in self.units(PROBE):
                                            if unit.tag in self.scouts_and_spots:
                                                continue

                                    await self.do(obs.move(location))
                                    self.scouts_and_spots[obs.tag] = location
                                    break
                            except Exception as e:
                                pass

        for obs in self.units(unit_type):
            if obs.tag in self.scouts_and_spots:
                if obs in [probe for probe in self.units(PROBE)]:
                    await self.do(obs.move(self.random_location_variance(self.scouts_and_spots[obs.tag])))


    async def intel(self):

        game_data = np.zeros((self.game_info.map_size[1], self.game_info.map_size[0], 3), np.uint8)


        for unit in self.units().ready:
            pos = unit.position
            cv2.circle(game_data, (int(pos[0]), int(pos[1])), int(unit.radius*8), (255, 255, 255), math.ceil(int(unit.radius*0.5)))


        for unit in self.known_enemy_units:
            pos = unit.position
            cv2.circle(game_data, (int(pos[0]), int(pos[1])), int(unit.radius*8), (125, 125, 125), math.ceil(int(unit.radius*0.5)))

        try:
            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

            worker_weight = len(self.units(PROBE)) / (self.supply_cap-self.supply_left)
            if worker_weight > 1.0:
                worker_weight = 1.0

            cv2.line(game_data, (0, 19), (int(line_max*worker_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
        except Exception as e:
            print(str(e))


        # flip horizontally to make our final fix in visual representation:
        grayed = cv2.cvtColor(game_data, cv2.COLOR_BGR2GRAY)
        self.flipped = cv2.flip(grayed, 0)

        resized = cv2.resize(self.flipped, dsize=None, fx=2, fy=2)

        if not HEADLESS:
            if self.use_model:
                cv2.imshow(str(self.title), resized)
                cv2.waitKey(1)
            else:
                cv2.imshow(str(self.title), resized)
                cv2.waitKey(1)

    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 build_scout(self):
        for rf in self.units(ROBOTICSFACILITY).ready.noqueue:
            print(len(self.units(OBSERVER)), self.time/3)
            if self.can_afford(OBSERVER) and self.supply_left > 0:
                await self.do(rf.train(OBSERVER))
                break
        if len(self.units(ROBOTICSFACILITY)) == 0:
            pylon = self.units(PYLON).ready.noqueue.random
            if self.units(CYBERNETICSCORE).ready.exists:
                if self.can_afford(ROBOTICSFACILITY) and not self.already_pending(ROBOTICSFACILITY):
                    await self.build(ROBOTICSFACILITY, near=pylon)


    async def build_worker(self):
        nexuses = self.units(NEXUS).ready.noqueue
        if nexuses.exists:
            if self.can_afford(PROBE):
                await self.do(random.choice(nexuses).train(PROBE))

    async def build_zealot(self):
        #if len(self.units(ZEALOT)) < (8 - self.time): # how we can phase out zealots over time?
        gateways = self.units(GATEWAY).ready.noqueue
        if gateways.exists:
            if self.can_afford(ZEALOT):
                await self.do(random.choice(gateways).train(ZEALOT))

    async def build_gateway(self):
        #if len(self.units(GATEWAY)) < 5:
        pylon = self.units(PYLON).ready.noqueue.random
        if self.can_afford(GATEWAY) and not self.already_pending(GATEWAY):
            await self.build(GATEWAY, near=pylon.position.towards(self.game_info.map_center, 5))

    async def build_voidray(self):
        stargates = self.units(STARGATE).ready.noqueue
        if stargates.exists:
            if self.can_afford(VOIDRAY):
                await self.do(random.choice(stargates).train(VOIDRAY))

    async def build_stalker(self):
        pylon = self.units(PYLON).ready.noqueue.random
        gateways = self.units(GATEWAY).ready
        cybernetics_cores = self.units(CYBERNETICSCORE).ready

        if gateways.exists and cybernetics_cores.exists:
            if self.can_afford(STALKER):
                await self.do(random.choice(gateways).train(STALKER))

        if not cybernetics_cores.exists:
            if self.units(GATEWAY).ready.exists:
                if self.can_afford(CYBERNETICSCORE) and not self.already_pending(CYBERNETICSCORE):
                    await self.build(CYBERNETICSCORE, near=pylon.position.towards(self.game_info.map_center, 5))

    async def build_assimilator(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 build_stargate(self):
        if self.units(PYLON).ready.exists:
            pylon = self.units(PYLON).ready.random
            if self.units(CYBERNETICSCORE).ready.exists:
                if self.can_afford(STARGATE) and not self.already_pending(STARGATE):
                    await self.build(STARGATE, near=pylon.position.towards(self.game_info.map_center, 5))

    async def build_pylon(self):
            nexuses = self.units(NEXUS).ready
            if nexuses.exists:
                if self.can_afford(PYLON) and not self.already_pending(PYLON):
                    await self.build(PYLON, near=self.units(NEXUS).first.position.towards(self.game_info.map_center, 5))

    async def expand(self):
        try:
            if self.can_afford(NEXUS) and len(self.units(NEXUS)) < 3:
                await self.expand_now()
        except Exception as e:
            print(str(e))

    async def do_nothing(self):
        wait = random.randrange(7, 100)/100
        self.do_something_after = self.time + wait

    async def defend_nexus(self):
        if len(self.known_enemy_units) > 0:
            target = self.known_enemy_units.closest_to(random.choice(self.units(NEXUS)))
            for u in self.units(VOIDRAY).idle:
                await self.do(u.attack(target))
            for u in self.units(STALKER).idle:
                await self.do(u.attack(target))
            for u in self.units(ZEALOT).idle:
                await self.do(u.attack(target))

    async def attack_known_enemy_structure(self):
        if len(self.known_enemy_structures) > 0:
            target = random.choice(self.known_enemy_structures)
            for u in self.units(VOIDRAY).idle:
                await self.do(u.attack(target))
            for u in self.units(STALKER).idle:
                await self.do(u.attack(target))
            for u in self.units(ZEALOT).idle:
                await self.do(u.attack(target))

    async def attack_known_enemy_unit(self):
        if len(self.known_enemy_units) > 0:
            target = self.known_enemy_units.closest_to(random.choice(self.units(NEXUS)))
            for u in self.units(VOIDRAY).idle:
                await self.do(u.attack(target))
            for u in self.units(STALKER).idle:
                await self.do(u.attack(target))
            for u in self.units(ZEALOT).idle:
                await self.do(u.attack(target))

    async def do_something(self):

        if self.time > self.do_something_after:
            if self.use_model:
                prediction = self.model.predict([self.flipped.reshape([-1, 176, 200, 3])])
                choice = np.argmax(prediction[0])
            else:
                worker_weight = 8
                zealot_weight = 3
                voidray_weight = 20
                stalker_weight = 8
                pylon_weight = 5
                stargate_weight = 5
                gateway_weight = 3

                choice_weights = 1*[0]+zealot_weight*[1]+gateway_weight*[2]+voidray_weight*[3]+stalker_weight*[4]+worker_weight*[5]+1*[6]+stargate_weight*[7]+pylon_weight*[8]+1*[9]+1*[10]+1*[11]+1*[12]+1*[13]
                choice = random.choice(choice_weights)

            try:
                await self.choices[choice]()
            except Exception as e:
                print(str(e))

            y = np.zeros(14)
            y[choice] = 1
            self.train_data.append([y, self.flipped])


while True:
    run_game(maps.get("AbyssalReefLE"), [
        Bot(Race.Protoss, SentdeBot()),
        #Bot(Race.Protoss, SentdeBot()),
        Computer(Race.Protoss, Difficulty.Easy)
        ], realtime=False)

Следующая статья — Python AI в StarCraft II. Часть XVII: продолжаем обучение.

machine learning logo

Английский для программистов

Наш телеграм канал с тестами по английскому языку для программистов. Английский это часть карьеры программиста. Поэтому полезно заняться им уже сейчас

×