class GA(object):
def __init__(self):
self.num_of_organisms = POPSIZE
self.survivors = ELITE
self.new_organisms = self.num_of_organisms - self.survivors
self.mutation_rate = MUTRATE
self.crossover_rate = CROSSRATE
#initialize the population
self.population = self.InitPop(self.num_of_organisms)
#keep track of which organism in the population we are working on
self.current_organism = 0
#keeps track of what generation we are on
self.current_generation = 0
#GA gets the application
self.sequenceType = SEQUENCE
self.seed = numpy.random.random()
self.app = TetrisApp(self)
#GA gets our agent, which needs the organism
#so it can access weights of the organism
self.ai = Agent(self.app)
self.app.ai = self.ai
self.cycleStart = 0
self.cycleEnd = 0
self.fitnessDictionary = {}
self.lastBest = []
def RandomOrganism(self):
nums = []
for j in range(0, 12):
a = numpy.random.uniform(LOWERBOUND, UPPERBOUND)
nums.append(a)
organism = Organism(nums)
self.normalize(organism)
return organism
def InitPop(self, populationSize):
#init population with a seed
#random.seed(7)
population = []
#for each organism in the population
#population.append(Organism([-0.25835108880355967, -0.18873479853738032, -0.6081190254748627, -0.5281331622290867, -0.0936639080926526, -0.10826897335053938, 0.15010957868145391, -0.21161009827721672, -0.04113776799016001, 0.2957493369775496, -0.07093022881256028, -0.2586553756116776]))
for i in range(0, populationSize):
organism = self.RandomOrganism()
population.append(organism)
#returns a list of a list of 4 bitarraysc\
return population
#start running the game
def Run(self):
with open(RESULTS, 'w') as f:
f.write(
"\n Cross Type: %s, Selection Type: %s, Crossover Rate: %s, Mutation Rate: %s , Replacement Per Cycle: %s\n Theoretical Line limit: %s "
% (CROSSTYPE, SELECTIONTYPE, self.crossover_rate,
self.mutation_rate, self.new_organisms,
(NUMGAMES * self.app.limit * 4 / 10)))
#all heuristics
#f.write("Weights: Aggregate Height, Bumpiness, Holes, LinesCleared, Connected Holes, Blockades, Altitude Delta, Weighted Blocks, H-Roughness, V-Roughness, Wells, Biggest Well, Total Height.\n Mutation Rate: %s , Replacement Per Cycle: %s\n" % (self.mutation_rate, self.new_organisms))
self.cycleStart = time.time()
self.app.run()
def NextAI(self):
self.current_organism += 1
#if we have worked on every organism in the current population, get the next
#generation
self.app.piecesPlayed = 0
if self.current_organism >= self.num_of_organisms:
self.current_organism = 0
self.NextGeneration()
#this is for if we keep the same sequence for every generation and each organism only plays 1 game - we can use this to skip playing the elites
if SEQUENCE == "fixed" and NUMGAMES == 1:
while self.current_organism < self.num_of_organisms and tuple(
self.population[self.current_organism].heuristics
) in self.fitnessDictionary.keys():
#print("ALREADY SEEN %s" % self.population[self.current_organism].heuristics)
self.population[
self.current_organism].fitness = self.fitnessDictionary[
tuple(
self.population[self.current_organism].heuristics)]
self.current_organism += 1
if self.current_organism >= self.num_of_organisms:
self.current_organism = 0
self.NextGeneration()
#update the heuristics for the organism we are working on
self.ai.heuristics = self.population[self.current_organism].heuristics
#handles when a game we are testing the current organism on ends
def GameOver(self, lines_cleared):
organism = self.population[self.current_organism]
organism.fitness += lines_cleared
#load the next organism into the algo
organism.played += 1
if organism.played == NUMGAMES:
self.fitnessDictionary[tuple(
organism.heuristics)] = organism.fitness
self.NextAI()
if SEQUENCE == "fixed":
self.app.start_game(self.seed)
elif SEQUENCE == "random":
self.app.start_game(numpy.random.random())
else:
#restart the game
self.app.piecesPlayed = 0
if SEQUENCE == "fixed":
self.app.start_game(self.seed)
elif SEQUENCE == "random":
self.app.start_game(numpy.random.random())
#add normalization
def normalize(self, org):
squared = []
for h in org.heuristics:
squared.append(h * h)
norm = numpy.sqrt(sum(squared))
for i, weight in enumerate(org.heuristics):
org.heuristics[i] /= norm
#check if the population has converged -- TOD0
#tournament selection might be more valuable. add this
def tournament(self):
indices = [i for i in range(0, len(self.population))]
#since the population is sorted, just select the two smallest indices from the pool.
v1 = None
v2 = None
x = int(self.num_of_organisms * .1)
for a in range(0, x):
selected = numpy.random.choice(indices)
if v1 == None or selected < v1:
v2 = v1
v1 = selected
elif v2 == None or selected < v2:
v2 = selected
return self.population[v1], self.population[v2]
#roulette selection
def roulette(self):
fSum = float(sum([org.fitness for org in self.population]))
relativeFitness = []
for x in range(0, len(self.population)):
relativeFitness.append(self.population[x].fitness / fSum)
#worse organisms are more likey to miss in roulette
probs = [
sum(relativeFitness[:i + 1]) for i in range(len(relativeFitness))
]
r = random.random()
for i, organism in enumerate(self.population):
if r <= probs[i]:
return organism
def NextGeneration(self):
self.population.sort(key=lambda x: x.fitness, reverse=True)
averageScore = 0
elite = self.population[:self.survivors]
for a in elite:
averageScore += a.fitness
averageScore = averageScore / len(elite)
self.cycleEnd = time.time() - self.cycleStart
#print the last generation out
with open(RESULTS, 'a') as f:
f.write(
"\nGeneration: %s, Sequence Type: %s, Cycle Time: %s Elite Average Lines Cleared in %s Games: %s\n"
% (self.current_generation, SEQUENCE,
str(datetime.timedelta(seconds=self.cycleEnd)), NUMGAMES,
averageScore))
for a in self.population:
f.write("%s, Age: %s Weights: %s - Lines Cleared:%s\n" %
(a.name, a.age, a.heuristics, a.fitness))
for key in self.fitnessDictionary.keys():
if key not in [tuple(org.heuristics) for org in self.population]:
del self.fitnessDictionary[key]
#increment the generation
self.current_generation += 1
#create the new population with only the survivors
self.SelectSurvivors()
eliteScores = [org.fitness for org in self.population[:self.survivors]]
if eliteScores == self.lastBest:
self.mutation_rate += .05
#create the new organisms to add to the new_pop
#roulette selction
if SELECTIONTYPE == "roulette":
for x in range(0, self.new_organisms):
#select two parents
parent1 = self.roulette()
parent2 = self.roulette()
while parent1 == parent2:
parent2 = self.roulette()
#print("p1: %s , p2: %s" % (parent1.name, parent2.name))
#create the new organism
a = self.Crossover(parent1, parent2)
#mutate the children
if numpy.random.random() < MUTRATE:
self.mutate(a)
#add to population
self.population.append(a)
elif SELECTIONTYPE == "tournament":
#tounament selection
for x in range(0, self.new_organisms):
p1, p2 = self.tournament()
while p1 == p2 or p1.fitness == 0 or p2.fitness == 0:
p1, p2 = self.tournament()
new = self.Crossover(p1, p2)
if numpy.random.random() < MUTRATE:
self.mutate(new)
self.population.append(new)
#reset the fitness to 0
for org in self.population:
org.played = 0
org.fitness = 0
self.lastBest = eliteScores
self.cycleStart = time.time()
#check to make sure we have the correct number of organisms in the new
#population
assert self.num_of_organisms == len(
self.population
), "ERROR: new population doesnt the correct number of organisms have %s, want %s" % (
len(self.population), self.num_of_organisms)
#Will return the survivors of a population, will return self.survivors number of organisms
def SelectSurvivors(self):
#sort the population by Organism.fitness
self.population.sort(key=lambda x: x.fitness, reverse=True)
#kill off amount needed to introduce specified amount of new organisms
self.population = self.population[:self.survivors]
for organism in self.population:
organism.age += 1
#takes two parents and does uniform crossover
#returns an Organism
def Crossover(self, parent1, parent2):
child = []
#two point
# add other crossover methods that can be specified at launch and crossover using the CROSSRATE
#uniform crossover
if CROSSTYPE == "uniform":
for x in range(0, len(parent1.heuristics)):
if numpy.random.random() < .5:
child.append(parent1.heuristics[x])
else:
child.append(parent2.heuristics[x])
elif CROSSTYPE == "average":
#weighted average crossover
a = parent1.fitness
b = parent2.fitness
for x in range(0, len(parent1.heuristics)):
child.append((a * parent1.heuristics[x]) +
(b * parent2.heuristics[x]))
offspring = Organism(child)
#print("CROSSOVER NORMALIZING %s" % offspring.heuristics)
self.normalize(offspring)
return offspring
#mutates the weights of a chromosome
def mutate(self, organism):
#mutation range of -.2 to +.2
mutation = numpy.random.random() * .4 - .2
#choose a random weight to mutate
x = numpy.random.randint(0, 12)
organism.heuristics[x] += mutation
self.normalize(organism)
def run_game(self, player, seed=10000, debug=False):
App = TetrisApp(player, seed=seed, debug=debug)
score = App.run()
return score
class GeneticAlgorithms(object):
def __init__(self):
self.app = TetrisApp(self)
self.ai = AI(self.app)
self.app.ai = self.ai
self.population = [self.random_chromosome() for _ in range(POPULATION_SIZE)]
self.current_chromosome = 0
self.current_generation = 1
self.ai.heuristics = self.population[self.current_chromosome].heuristics
def run(self):
self.app.run()
def next_ai(self):
self.current_chromosome += 1
if self.current_chromosome >= POPULATION_SIZE:
self.current_chromosome = 0
self.next_generation()
self.ai.heuristics = self.population[self.current_chromosome].heuristics
def on_game_over(self, score):
chromosome = self.population[self.current_chromosome]
chromosome.games += 1
chromosome.total_fitness += score
if chromosome.games % GAMES_TO_AVG == 0:
self.next_ai()
self.app.start_game()
def population_has_converged(self):
t = CONVERGED_THRESHOLD
pop = self.population
return all(all(pop[0].heuristics[f]-t < w < pop[0].heuristics[f]+t for f, w in c.heuristics.items()) for c in pop)
def next_generation(self):
print("__________________\n")
if self.population_has_converged():
print("Population has converged on generation %s.\n values: %s"
% (self.current_generation, [(f.__name__, w) for f, w in self.population[0].heuristics.items()]))
sys.exit()
print("GENERATION %s COMPLETE" % self.current_generation)
print("AVG FITNESS", sum([c.avg_fitness() for c in self.population]) / POPULATION_SIZE)
self.current_generation += 1
for c in self.population:
print("chromosome", c.name, "fitness", c.avg_fitness())
best_chromosome = max(self.population, key=lambda c: c.avg_fitness())
print("Fittest chromosome:", best_chromosome.name, "fitness", best_chromosome.avg_fitness(), "\n%s" % [(f.__name__, w) for f, w in best_chromosome.heuristics.items()])
print("\nEVOLUTION")
new_population = self.selection(SURVIVORS_PER_GENERATION, SELECTION_METHOD)
for c in new_population:
print("chromosome", c.name, "fitness", c.avg_fitness(), "SURVIVED")
for _ in range(NEWBORNS_PER_GENERATION):
parents = self.selection(2, SELECTION_METHOD)
new_population.append(self.crossover(parents[0], parents[1], CROSSOVER_METHOD))
print(parents[0].name, "and", parents[1].name, "PRODUCED", new_population[-1].name)
for _ in range(MUTATION_PASSES):
for chromosome in new_population:
self.mutation(chromosome, MUTATION_RATE / MUTATION_PASSES)
print("__________________\n")
assert len(new_population) == len(self.population), "SURVIVORS_PER_GENERATION + NEWBORNS_PER_GENERATION != POPULATION_SIZE"
self.population = new_population
def selection(self, num_selected, method):
def roulette(population):
total_fitness = sum([c.avg_fitness() for c in population])
winner = randrange(int(total_fitness))
fitness_so_far = 0
for chromosome in population:
fitness_so_far += chromosome.avg_fitness()
if fitness_so_far > winner:
return chromosome
if method == SelectionMethod.roulette:
survivors = []
for _ in range(num_selected):
survivors.append(roulette([c for c in self.population if c not in survivors]))
return survivors
raise ValueError('SelectionMethod %s not implemented' % method)
def crossover(self, c1, c2, method):
def random_attributes():
heuristics = {}
for fun, _ in c1.heuristics.items():
heuristics[fun] = random.choice((c1, c2)).heuristics[fun]
return Chromosome(heuristics)
def average_attributes():
heuristics = {}
for fun, _ in c1.heuristics.items():
heuristics[fun] = (c1.heuristics[fun] + c2.heuristics[fun]) / 2
return Chromosome(heuristics)
if method == CrossoverMethod.random_attributes:
return random_attributes()
if method == CrossoverMethod.average_attributes:
return average_attributes()
raise ValueError('CrossoverMethod %s not implemented' % method)
def mutation(self, chromosome, mutation_rate):
if randint(0, int(mutation_rate)) == 0:
h = chromosome.heuristics
h[random.choice(list(h.keys()))] = randrange(-1000, 1000)
print(chromosome.name, "MUTATED")
def random_chromosome(self):
return Chromosome({fun: randrange(-1000, 1000) for fun, weight in self.ai.heuristics.items()})
