I created an AI in python/pygame but even after spending hours of debugging, I could not find why the individuals(dots) are not getting mutated. After few generations, all the individuals just overlap each other and follow the same exact path. But after mutation they should move a little bit differently.
Here is what a population size of 10 looks like after every 2-3 generations..
As you can see, just after few generations they just overlap and all the individuals in the population move together, following exact same path! We need mutations!!!
I would be really grateful to you if you could find any mistake. Thank!
I saw the code from: https://www.youtube.com/watch?v=BOZfhUcNiqk&t and tried to make it in python. Here's my code
import pygame, random
import numpy as np
pygame.init()
width = 800
height = 600
screen = pygame.display.set_mode((width, height))
pygame.display.set_caption("The Dots")
FPS = 30
clock = pygame.time.Clock()
gameExit = False
grey = [30, 30, 30]
white = [255, 255, 255]
black = [0, 0, 0]
red = [255, 0, 0]
goal = [400, 10]
class Dot():
def __init__(self):
self.x = int(width/2)
self.y = int(height - 150)
self.r = 3
self.c = black
self.xVel = self.yVel = 0
self.xAcc = 0
self.yAcc = 0
self.dead = False
self.steps = 0
self.reached = False
self.brain = Brain(200)
def show(self):
pygame.draw.circle(screen, self.c, [int(self.x), int(self.y)], self.r)
def update(self):
if (self.x >= width or self.x <= 0 or self.y >= height or self.y <= 0):
self.dead = True
elif (np.sqrt((self.x-goal[0])**2 + (self.y-goal[1])**2) < 5):
self.reached = True
if not self.dead and not self.reached:
if len(self.brain.directions) > self.steps:
self.xAcc = self.brain.directions[self.steps][0]
self.yAcc = self.brain.directions[self.steps][1]
self.steps += 1
self.xVel += self.xAcc
self.yVel += self.yAcc
if self.xVel > 5:
self.xVel = 5
if self.yVel > 5:
self.yVel = 5
self.x += self.xVel
self.y += self.yVel
else: self.dead = True
def calculateFitness(self):
distToGoal = np.sqrt((self.x-goal[0])**2 + (self.y-goal[1])**2)
self.fitness = 1/(distToGoal**2)
return self.fitness
def getChild(self):
child = Dot()
child.brain = self.brain
return child
class Brain():
def __init__(self, size):
self.size = size
self.directions = []
self.randomize()
def randomize(self):
self.directions.append((np.random.normal(size=(self.size, 2))).tolist())
self.directions = self.directions[0]
def mutate(self):
for i in self.directions:
rand = random.random()
if rand < 1:
i = np.random.normal(size=(1, 2)).tolist()[0]
class Population():
def __init__(self, size):
self.size = size
self.dots = []
self.fitnessSum = 0
for i in range(self.size):
self.dots.append(Dot())
def show(self):
for i in self.dots:
i.show()
def update(self):
for i in self.dots:
i.update()
def calculateFitness(self):
for i in self.dots:
i.calculateFitness()
def allDead(self):
for i in self.dots:
if not i.dead and not i.reached:
return False
return True
def calculateFitnessSum(self):
self.fitnessSum = 0
for i in self.dots:
self.fitnessSum += i.fitness
def SelectParent(self):
rand = random.uniform(0, self.fitnessSum)
runningSum = 0
for i in self.dots:
runningSum += i.fitness
if runningSum > rand:
return i
def naturalSelection(self):
newDots = []
self.calculateFitnessSum()
for i in self.dots:
parent = self.SelectParent()
newDots.append(parent.getChild())
self.dots = newDots
def mutate(self):
for i in self.dots:
i.brain.mutate()
test = Population(100)
while not gameExit:
for event in pygame.event.get():
if event.type == pygame.QUIT:
gameExit = True
screen.fill(white)
if test.allDead():
#Genetic Algorithm
test.calculateFitness()
test.naturalSelection()
test.mutate()
else:
test.update()
test.show()
pygame.draw.circle(screen, red, goal, 4)
clock.tick(FPS)
pygame.display.update()
pygame.quit()
Thanks for any help!
