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update: horses now probably learn

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KOSMOGOR
2025-04-10 23:34:47 +03:00
parent 0128f33f44
commit e6735af700
3 changed files with 29 additions and 19 deletions
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gameobjects.py
+3 -1
View File
@@ -46,7 +46,7 @@ class GameObject:
self.image.set_colorkey(None) # Explicitly disable colorkey
class Horse(GameObject):
default_vacceleration = 0.5 # Default acceleration value
default_vacceleration = 0.3 # Default acceleration value
vspeed = 0 # Vertical speed
vacceleration = 0 # Vertical acceleration
stopped = False # Whether the horse is stopped
@@ -106,6 +106,8 @@ class Horse(GameObject):
def count_fitness(self):
if self.stopped == False:
self.fitness += 1
if self.vacceleration == 0:
self.fitness -= 0.9
class Background(GameObject):
def __init__(self, image_path, x, y):
genetic_alg.py
+18 -11
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@@ -5,9 +5,9 @@ import numpy as np
class NeuralNetwork(nn.Module):
def __init__(self, inputSize):
super().__init__()
self.hidden1 = nn.Linear(inputSize, 32)
self.hidden2 = nn.Linear(32, 16)
self.output = nn.Linear(16, 3)
self.hidden1 = nn.Linear(inputSize, 128)
self.hidden2 = nn.Linear(128, 128)
self.output = nn.Linear(128, 3)
def forward(self, x):
x = torch.relu(self.hidden1(x))
@@ -16,20 +16,25 @@ class NeuralNetwork(nn.Module):
return torch.argmax(x)
class GeneticAlgorithm:
def __init__(self, populationSize: int, mutationRate: float, percentageBest: float, inputSize: int = 5):
def __init__(self, populationSize: int, mutationRate: float, percentageBest: float, percentageNew: float, inputSize: int = 5):
self.populationSize = populationSize
self.mutationRate = mutationRate
self.percentageBest = percentageBest
self.percentageNew = percentageNew
self.inputSize = inputSize
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(self.device)
self.initialize_population()
def create_ind(self):
return NeuralNetwork(self.inputSize).to(self.device)
def initialize_population(self):
self.population = [NeuralNetwork(self.inputSize).to(self.device) for _ in range(self.populationSize)]
self.population = [self.create_ind() for _ in range(self.populationSize)]
def crossover(self, parent1: NeuralNetwork, parent2: NeuralNetwork):
child1 = NeuralNetwork(self.inputSize).to(self.device)
child2 = NeuralNetwork(self.inputSize).to(self.device)
child1 = self.create_ind()
child2 = self.create_ind()
point1 = len(child1.hidden1.weight.data) // 2
point2 = len(child1.hidden2.weight.data) // 2
child1.hidden1.weight.data = torch.cat((parent1.hidden1.weight.data[:point1], parent2.hidden1.weight.data[point1:]), dim=0)
@@ -44,21 +49,23 @@ class GeneticAlgorithm:
def mutate(self, model: NeuralNetwork):
for param in model.parameters():
if torch.rand(1).item() < self.mutationRate:
param.data += torch.randn_like(param.data) * 0.1
param.data += torch.randn_like(param.data) * 0.1 * (1 if torch.rand(1).item() >= 0.5 else -1)
return model
def learn(self, fitness: list):
self.population = [self.population[x] for x in np.argsort(fitness)]
self.population = [self.population[x] for x in np.argsort(fitness)[::-1]]
numBest = int(self.populationSize * self.percentageBest)
self.population = self.population[:numBest]
while len(self.population) < self.populationSize:
while len(self.population) < self.populationSize - self.populationSize * self.percentageNew:
parent1, parent2 = np.random.choice(self.population), np.random.choice(self.population)
child1, child2 = self.crossover(parent1, parent2)
child1 = self.mutate(child1)
child2 = self.mutate(child2)
self.population.extend([child1, child2])
while len(self.population) < self.populationSize: self.population.append(self.create_ind())
while len(self.population) > self.populationSize: self.population.pop()
def predict(self, data: list, i):
data = torch.tensor(data, requires_grad=False).float().to(self.device)
return self.population[i](data)
return self.population[i](data)
main.py
+8 -7
View File
@@ -3,10 +3,13 @@ import sys
from gameobjects import *
from genetic_alg import GeneticAlgorithm
POPULATION_SIZE = 20
POPULATION_SIZE = 50
MUTATION_RATE = 0.5
POPULATION_NEW = 0.1
POPULATION_BEST = 0.2
FRAME_RATE = 160 #TODO: FIX THE INCORRECT FRAME RATE CORRELATION
BARRIER_SPEED = 10
BARRIER_DELAY = 100
pygame.init()
@@ -32,8 +35,6 @@ def init_game():
grass = Background("images/Grass.jpg", 0, 0)
grass.set_size(WIDTH, HEIGHT)
BARRIER_SPEED = 25
gameobjects = []
barriers = []
@@ -46,7 +47,7 @@ def init_game():
horse.set_vspeed(0)
horse.frame_counter = 0
horse.fitness = 0
spawner = Spawner("images/Barrier.png", 125)
spawner = Spawner("images/Barrier.png", BARRIER_DELAY)
new_barrier = spawner.spawn()
barriers.append(new_barrier)
gameobjects.extend(horses)
@@ -65,12 +66,12 @@ def get_features(horse):
last_barrier.rect.bottomright[0], last_barrier.rect.bottomright[1],
horse.rect.topleft[0], horse.rect.topleft[1],
horse.rect.bottomright[0], horse.rect.bottomright[1],
HEIGHT, BARRIER_SPEED]
0, HEIGHT, BARRIER_SPEED]
return features
genecticAlg = GeneticAlgorithm(POPULATION_SIZE, MUTATION_RATE, POPULATION_BEST, 10)
horses = [Horse("images/Horse_1.png", 50, HEIGHT/2 + 0 * i) for i in range(POPULATION_SIZE)]
init_game()
genecticAlg = GeneticAlgorithm(POPULATION_SIZE, MUTATION_RATE, POPULATION_BEST, POPULATION_NEW, len(get_features(horses[0])))
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT: # Handle window close event
@@ -115,7 +116,7 @@ while True:
if horse.rect.colliderect(upper_bound_rect) or horse.rect.colliderect(lower_bound_rect):
horse.stop()
horse.count_fitness()
print(str(horse.color) + ': ' + str(horse.fitness))
# print(str(horse.color) + ': ' + str(horse.fitness))
for object in gameobjects: