def __init__(self, genetic, squere=3):
self.squere = squere
self.shape = 0
self.frame_size_x = 210
self.frame_size_y = 210
self.check_errors = pygame.init()
if self.check_errors[1] > 0:
print(f'[!] Had {self.check_errors[1]} errors when initialising game, exiting...')
sys.exit(-1)
else:
print('[+] Game successfully initialised')
pygame.display.set_caption('Snake Eater')
self.game_window = pygame.display.set_mode((self.frame_size_x, self.frame_size_y))
self.black = pygame.Color(0, 0, 0)
self.white = pygame.Color(254, 254, 254)
self.red = pygame.Color(255, 0, 0)
self.green = pygame.Color(0, 255, 0)
self.blue = pygame.Color(0, 0, 255)
self.fps_controller = pygame.time.Clock()
self.snake_pos = [100, 50]
self.snake_body = [[100, 50], [100 - 10, 50], [100 - (2 * 10), 50]]
self.snake_on_eat = False
self.food_pos = [self.snake_pos[0] + 10, 50] # [random.randrange(1, (self.frame_size_x // 10)) * 10, random.randrange(1, (self.frame_size_y // 10)) * 10]
self.food_spawn = True
self.score = 0
self.create = True
self.reward = 0
self.genetic = genetic
self.rewards = []
self.individuum = []
self.screen1 = np.zeros((21, 21, 3), dtype=int)
self.dict_len = {}
for i in range(22):
if i % 2 != 0 and i != 0 and i != 1:
self.dict_len[i] = -10 * ((i - 1) // 2)
self.metrix = []
y = 10
for i in range(1, squere+1):
x = self.dict_len[squere]
for j in range(1, squere+1):
self.metrix.append([x, y])
x += 10
y -= 10class Game:
def is_true(self):
self.food_pos = [random.randrange(1, (self.frame_size_x // 10)) * 10,
random.randrange(1, (self.frame_size_y // 10)) * 10]
for body in self.snake_body:
if self.food_pos[0] == body[0] and self.food_pos[1] == body[1]:
self.is_true()
return self.food_pos
def add_reward(self, rew, food_aet=False, score=0, hit_wall=False, hit_self=False, hit_self_dur=False):
return rew(food_aet=food_aet, score=score, hit_wall=hit_wall, hit_self=hit_self, hit_self_dur=hit_self_dur)
def compile(self, optimizer, patience, screen, model, custom_loss, reward, epochs=1):
model.compile(loss=lambda y_true, y_pred: custom_loss(y_true, y_pred, reward),
optimizer=keras.optimizers.Adam(optimizer))
history = model.fit(np.expand_dims(screen, axis=0), np.zeros((1, 4)), epochs=epochs, verbose=0,
callbacks=[keras.callbacks.EarlyStopping(monitor='loss', patience=patience)],
sample_weight=np.array([reward]))
def starter(self, snake, optimizer):
self.game(model=snake.model, calculate_reward=snake.calculate_reward,
custom_loss=snake.custom_loss, neural_network=snake.neural_network,
build=snake.build_neural_network, snake=snake, optimizer=optimizer)
def game(self, model, calculate_reward, custom_loss, neural_network, build, snake, optimizer, difficulty=500):
direction = 'RIGHT'
change_to = direction
individ = []
if not self.create:
self.rewards.append(self.reward)
for weights in snake.get_weights():
individ.append([np.array(weights)])
self.individuum.append(individ)
self.genetic.add(individ)
self.reward = 0
self.snake_pos = [30, 50]
self.snake_body = [[30, 50], [30 - 10, 50], [30 - (2 * 10), 50]]
self.food_pos = [self.snake_pos[0] + 10, 50]
self.food_spawn = True
print(self.rewards)
genetic_result = self.genetic.get_child(rewards=self.rewards, snakes=self.individuum)
if genetic_result != None and genetic_result[0]:
print(f'Поколение: {self.genetic.generic}')
print(f'Лутший результат: {genetic_result[1]}')
snake.set_weights(genetic_result[1])
elif genetic_result != None and genetic_result[1]:
print("Все поколения пройдены!")
print(f'Лутший результат: {self.genetic.population[self.genetic.population.index(max(self.genetic.population))]}')
while True:
self.snake_on_eat = False
screen = []
for i in range(self.squere ** 2):
if self.snake_body[0][0] - self.metrix[i][0] == self.food_pos[0] and self.snake_body[0][1] - \
self.metrix[i][1] == self.food_pos[1]:
screen.append(1)
print("I`m here")
elif self.snake_body[0][0] - self.metrix[i][0] <= 0 or self.snake_body[0][1] - self.metrix[i][1] <= 0 or self.snake_body[0][0] - self.metrix[i][0] >= self.frame_size_x or self.snake_body[0][1] - self.metrix[i][1] >= self.frame_size_y or \
self.snake_body[0][0] - self.metrix[i][0] == self.snake_body[0][0] and self.snake_body[0][1] - \
self.metrix[i][1] == self.snake_body[0][1] or self.snake_body[0][0] - self.metrix[i][0] == \
self.snake_body[0][0] - 10 and self.snake_body[0][1] - self.metrix[i][1] == self.snake_body[0][
1] or self.snake_body[0][0] - self.metrix[i][0] == self.snake_body[0][0] + 10 and \
self.snake_body[0][1] - self.metrix[i][1] == self.snake_body[0][1] or self.snake_body[0][0] - \
self.metrix[i][0] == self.snake_body[0][0] and self.snake_body[0][1] - self.metrix[i][1] == \
self.snake_body[0][1] - 10 or self.snake_body[0][0] - self.metrix[i][0] == self.snake_body[0][
0] and self.snake_body[0][1] - self.metrix[i][1] == self.snake_body[0][1] + 10:
screen.append(-1)
else:
screen.append(0)
screen = np.array(screen)
if self.create:
build(optimizer, self.squere)
self.create = False
direct = neural_network(np.expand_dims(screen, axis=0)[0], model)
print(snake.get_weights()[0], '\n', snake.get_weights()[1])
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
elif event.type == pygame.KEYDOWN:
if event.key == ord('k'):
self.score = 0
self.reward += self.add_reward(rew=calculate_reward, hit_self_dur=True)
self.compile(optimizer=optimizer, patience=80, screen=screen, model=model, custom_loss=custom_loss, reward=self.reward)
self.starter(snake, optimizer)
elif event.key == ord("w"):
direct = 0
elif event.key == ord("s"):
direct = 1
elif event.key == ord("a"):
direct = 2
elif event.key == ord("d"):
direct = 3
if direct == 0 and change_to != 'DOWN':
change_to = 'UP'
if direct == 1 and change_to != 'UP':
change_to = 'DOWN'
if direct == 2 and change_to != 'RIGHT':
change_to = 'LEFT'
if direct == 3 and change_to != 'LEFT':
change_to = 'RIGHT'
if change_to == 'UP':
direction = 'UP'
if change_to == 'DOWN':
direction = 'DOWN'
if change_to == 'LEFT':
direction = 'LEFT'
if change_to == 'RIGHT':
direction = 'RIGHT'
if direction == 'UP':
self.snake_pos[1] -= 10
if direction == 'DOWN':
self.snake_pos[1] += 10
if direction == 'LEFT':
self.snake_pos[0] -= 10
if direction == 'RIGHT':
self.snake_pos[0] += 10
self.snake_body.insert(0, list(self.snake_pos))
if self.snake_pos[0] == self.food_pos[0] and self.snake_pos[1] == self.food_pos[1]:
self.score += 1
self.snake_on_eat = True
self.reward = self.add_reward(rew=calculate_reward, food_aet=self.snake_on_eat, score=self.score)
self.compile(optimizer=optimizer, patience=80, screen=screen, model=model, custom_loss=custom_loss, reward=self.reward)
self.food_spawn = False
else:
self.snake_body.pop()
if not self.food_spawn:
self.is_true()
self.food_spawn = True
self.game_window.fill(self.black)
for pos in self.snake_body:
pygame.draw.rect(self.game_window, self.red, pygame.Rect(pos[0], pos[1], 10, 10))
pygame.draw.rect(self.game_window, self.white, pygame.Rect(self.food_pos[0], self.food_pos[1], 10, 10))
if self.snake_pos[0] < 0 or self.snake_pos[0] > self.frame_size_x - 10:
print(f'Онполучил: {self.score}\nВін врізався в стінку!')
self.score = 0
self.reward += self.add_reward(rew=calculate_reward, hit_wall=True)
self.compile(optimizer=optimizer, patience=80, screen=screen, model=model, custom_loss=custom_loss, reward=self.reward)
self.starter(snake, optimizer)
if self.snake_pos[1] < 0 or self.snake_pos[1] > self.frame_size_y - 10:
print(f'Он получил: {self.score}\nВін врізався в стінку!!')
self.score = 0
self.reward += self.add_reward(rew=calculate_reward, hit_wall=True)
self.compile(optimizer=optimizer, patience=80, screen=screen, model=model, custom_loss=custom_loss, reward=self.reward)
self.starter(snake, optimizer)
for block in self.snake_body[1:]:
if self.snake_pos[0] == block[0] and self.snake_pos[1] == block[1]:
print(f'Він отримав: {self.score}\nВін сам себе вбив!')
self.score = 0
self.reward += self.add_reward(rew=calculate_reward, hit_self_dur=True)
self.compile(optimizer=optimizer, patience=80, screen=screen, model=model, custom_loss=custom_loss, reward=self.reward)
self.starter(snake, optimizer)
pygame.display.update()
self.fps_controller.tick(difficulty)class Genetic:
def __init__(self):
self.population_size = 20
self.population = []
self.v_crossover = 0.8
self.v_mutation = 0.2
self.generics = 300
self.generic = 0
self.get_ready = False
self.elite = 2
self.cicle = 0
def add(self, hromosom):
self.population.append(hromosom)
if self.cicle != self.population_size:
self.cicle += 1
else:
self.generic += 1
def calculate_elite(self, rewards, mobs):
elit = []
elit_rewards = []
max_rate = 0
pointer = 0
for index, reward in enumerate(rewards):
if pointer <= self.elite:
if reward > max_rate:
max_rate = reward
elit_rewards.append([index, reward])
pointer += 1
for i in elit_rewards:
elit.append(mobs[i[0]])
return elit
def crossover(self, hromosoms_of_elite):
childs = []
for hromosom in range(0, len(hromosoms_of_elite), 2):
if hromosom + 1 < len(hromosoms_of_elite):
child = []
for p in range(min(len(hromosoms_of_elite[hromosom]), len(hromosoms_of_elite[hromosom + 1]))):
gene = []
for w in range(min(len(hromosoms_of_elite[hromosom][p]), len(hromosoms_of_elite[hromosom + 1][p]))):
if self.v_crossover * 10 >= random.randint(0, 11):
gene.append(hromosoms_of_elite[hromosom + 1][p][w])
else:
gene.append(hromosoms_of_elite[hromosom][p][w])
child.append(gene)
childs.append(child)
return childs
def mutation(self, hromosoms):
childs = []
for hromosom in hromosoms:
for p in range(len(hromosom)):
for w in range(len(hromosom[p])):
if self.v_mutation * 10 >= random.randint(0, 11):
if 0 == random.randint(0, 1):
hromosoms[hromosom][p][w] -= 2.75
else:
hromosoms[hromosom][p][w] += 2.75
childs.append(hromosoms[hromosom])
return childs
def get_child(self, rewards, snakes):
if self.generic == self.generics:
self.get_ready = True
return [False, self.get_ready]
if self.cicle == self.population_size:
return [True, self.mutation(self.crossover(self.calculate_elite(rewards, snakes)))]
class Network:
def get_weights(self):
return self.model.get_weights()
def set_weights(self, weights):
return self.model.set_weights(weights)
def get_Total_Weights(self):
return self.model.count_params()
def calculate_reward(self, food_aet=False, score=0, hit_wall=False, hit_self=False, hit_self_dur=False):
if food_aet:
print('Молодець')
return 1.0 * score
elif hit_self_dur:
print('Дebilchik!')
return -5.0
elif hit_wall or hit_self:
print('Дурачок!')
return -0.9
else:
return 0.0
def custom_loss(self, y_true, y_pred, reward):
loss = y_true * (reward - y_pred) + (1 - y_true) * (reward + y_pred)
return loss
def build_neural_network(self, optimizer, squere, num_actions=4):
self.model.add(Flatten(input_shape=(squere**2, )))
self.model.add(Dense(units=4, activation="linear"))
self.model.add(Dense(units=num_actions, activation="linear"))
self.model.compile(loss=self.custom_loss,
optimizer=keras.optimizers.Adam(learning_rate=optimizer))
def neural_network(self, displaylist, model):
direct = model.predict(np.expand_dims(displaylist, axis=0), verbose=0)
return np.argmax(direct)
def __init__(self):
self.model = keras.Sequential()
genetic = Genetic()
game = Game(genetic)
snake = Network()
game.game(model=snake.model, calculate_reward=snake.calculate_reward, custom_loss=snake.custom_loss, neural_network=snake.neural_network, build=snake.build_neural_network, snake=snake, optimizer=0.4)