class Agent:
def __init__(self, carX, carY, window, goal, best=False):
self.car = Car(carX, carY)
self.window = window
self.dead = self.car.dead
self.finished = self.car.finished
self.fitness = 0
self.brain = Brain(5000)
self.goal = goal
self.bestCar = best
def draw(self, batch, foreground, background, vertices, show):
car = self.car.draw(batch, foreground, self.bestCar)
if show:
eyes = self.car.eyes(batch, background)
hitbox = self.car.hitbox(batch, background)
intersectEyes = self.car.intersectEyes(batch, vertices, background)
return car, eyes, hitbox, intersectEyes
return car
def generateHitbox(self):
hitbox = self.car.generateHitbox()
return hitbox
def move(self, dt):
if (self.brain.step < len(self.brain.instructions)):
instruction = self.brain.instructions[self.brain.step]
self.brain.step += 1
self.car.updateGA(dt, instruction)
else:
self.dead = True
def update(self, dt):
if not self.dead and not self.finished:
self.move(dt)
pos = self.car.position
if (pos.x < 2 or pos.y < 2 or pos.x > self.window.x - 2
or pos.y > self.window.y - 2):
self.dead = True
elif (abs(self.goal - pos) < 10):
self.finished = True
else:
self.car.dead = self.dead
self.car.finished = self.finished
def calcFitness(self):
if self.finished:
self.fitness = 1 / 16 + 10000 / (self.brain.step**2)
else:
self.fitness = 1 / (abs(self.goal - self.car.position)**2)
def clone(self, carX, carY, best=False):
baby = Agent(carX, carY, window=self.window, goal=self.goal, best=best)
baby.brain = self.brain.clone()
return baby
class Agent:
def __init__(self, carX, carY, window, best=False, scale: float = 1.48):
self.car = Car(carX, carY, scale)
self.scale = scale
self.window = window
self.dead = self.car.dead
self.fitness = 0
self.step = 0
self.maxStep = 500
self.reset = False
self.reset2 = False
self.nn = NeuralNetwork()
self.bestCar = best
self.index = 0
def draw(self, batch, foreground, background, vertices, show):
car = self.car.draw(batch, foreground, self.bestCar)
# intersectEyes = self.car.intersectEyes(batch, vertices, background)
# if show or not self.dead:
# eyes = self.car.eyes(batch, background)
# hitbox = self.car.hitbox(batch, background)
# return car, intersectEyes, eyes, hitbox
# return car, intersectEyes, eyes
# return car, intersectEyes
return car
def generateHitbox(self):
hitbox = self.car.generateHitbox()
return hitbox
def move(self, dt, vertices):
if self.step < self.maxStep:
inputnn = []
self.car.mathIntersect(vertices)
inputnn = self.car.observe()
if len(inputnn) > 7:
instruction = self.nn.feedforward(inputnn)
else:
instruction = 3
print(f"Input: {inputnn}")
self.step += 1
self.car.updateWithInstruction(dt, instruction)
else:
self.dead = True
def update(self, dt, vertices):
if not self.dead:
if self.car.currentCheckpoint > 1 and self.reset == False:
self.maxStep += 1000
self.reset = True
if self.car.currentCheckpoint > 2 and self.reset2 == False:
self.maxStep += 1000
self.reset2 = True
self.move(dt, vertices)
pos = self.car.position
if (pos.x < 2 or pos.y < 2 or pos.x > self.window.x - 2
or pos.y > self.window.y - 2):
self.dead = True
else:
self.car.dead = self.dead
def calcFitness(self, skeletonLines, checkpoints, blindSpot, blindIndex):
if self.car.currentCheckpoint > 0 or self.car.currentLap > 0:
if self.car.currentCheckpoint >= checkpoints:
self.car.currentLap += 1
self.car.currentCheckpoint = 0
minimum = 100000
minLine = None
for line in skeletonLines:
distance = d if (d := line.distance(
self.car.position)) != None else 99999
if distance < minimum:
minimum = distance
minLine = line
if minimum > 80:
minDistance = 100000
minSpot = None
for spot in blindSpot:
distance = abs(self.car.position - spot)
if distance < minDistance:
minDistance = distance
minSpot = spot
indexSpot = blindSpot.index(minSpot)
index = blindIndex[indexSpot]
minLine = skeletonLines[index]
else:
index = skeletonLines.index(minLine)
linesToIndex = skeletonLines[:index]
distanceToIndex = 0
for line in linesToIndex:
startPointLine, endPointLine = line.getEndPoints()
distanceToIndex += abs(endPointLine - startPointLine)
lineToOutside = linline.fromVector(minLine.n,
self.car.position) #BC
intersection = lineToOutside.intersect(minLine) #B
startPoint, _ = minLine.getEndPoints() #A
if startPoint.x == None:
distanceLine = 0
elif startPoint.y == None:
distanceLine = 0
elif intersection == None:
distanceLine = 0
else:
distanceLine = intersection - startPoint #AB
if index > 10 and self.car.currentCheckpoint < 3:
self.fitness = 0
else:
self.fitness = ((self.car.currentLap * 100) +
(abs(distanceLine) * 100) +
distanceToIndex * 100)**2 / self.step
self.index = index
else: