def main():
rng = NEAT.RNG()
rng.TimeSeed()
params = NEAT.Parameters()
params.PopulationSize = 128
params.DynamicCompatibility = True
params.WeightDiffCoeff = 1.0
params.CompatTreshold = 2.0
params.YoungAgeTreshold = 15
params.SpeciesMaxStagnation = 15
params.OldAgeTreshold = 35
params.MinSpecies = 5
params.MaxSpecies = 10
params.RouletteWheelSelection = False
params.Elitism = True
params.RecurrentProb = 0.5
params.OverallMutationRate = 0.2
params.MutateWeightsProb = 0.8
# params.MutateNeuronTimeConstantsProb = 0.1
# params.MutateNeuronBiasesProb = 0.1
params.WeightMutationMaxPower = 0.5
params.WeightReplacementMaxPower = 1.0
params.MutateWeightsSevereProb = 0.5
params.WeightMutationRate = 0.25
params.TimeConstantMutationMaxPower = 0.1
params.BiasMutationMaxPower = params.WeightMutationMaxPower
params.MaxWeight = 8
params.MutateAddNeuronProb = 0.1
params.MutateAddLinkProb = 0.2
params.MutateRemLinkProb = 0.0
params.MutateActivationAProb = 0.0;
params.ActivationAMutationMaxPower = 0.5;
params.MinActivationA = 1.1
params.MaxActivationA = 6.9
params.MinNeuronTimeConstant = 0.04
params.MaxNeuronTimeConstant = 0.24
params.MinNeuronBias = -params.MaxWeight
params.MaxNeuronBias = params.MaxWeight
params.MutateNeuronActivationTypeProb = 0.0
params.ActivationFunction_SignedSigmoid_Prob = 0
params.ActivationFunction_UnsignedSigmoid_Prob = 0
params.ActivationFunction_Tanh_Prob = 1
params.ActivationFunction_TanhCubic_Prob = 0
params.ActivationFunction_SignedStep_Prob = 0
params.ActivationFunction_UnsignedStep_Prob = 0
params.ActivationFunction_SignedGauss_Prob = 0
params.ActivationFunction_UnsignedGauss_Prob = 0
params.ActivationFunction_Abs_Prob = 0
params.ActivationFunction_SignedSine_Prob = 0
params.ActivationFunction_UnsignedSine_Prob = 0
params.ActivationFunction_Linear_Prob = 0
params.CrossoverRate = 0.75 # mutate only 0.25
params.MultipointCrossoverRate = 0.4
params.SurvivalRate = 0.2
params.MutateNeuronTraitsProb = 0
params.MutateLinkTraitsProb = 0
trials = 5
generations = 10
g = NEAT.Genome(
0,
24 + 1 + 1,
0,
4,
False,
NEAT.ActivationFunction.TANH,
NEAT.ActivationFunction.TANH,
0,
params,
0,
1)
pop = NEAT.Population(g, params, True, 1.0, rnd.randint(0, 1000))
hof = []
maxf_ever = 0
env = gym.make('BipedalWalker-v3')
try:
for generation in range(generations):
fitnesses = []
#args = [x for x in NEAT.GetGenomeList(pop)]
#dv.block=True
#fitnesses = dv.map_sync(evaluate_genome, args)
for _, genome in tqdm(enumerate(NEAT.GetGenomeList(pop))):
fitness = evaluate_genome(env, genome, trials)
fitnesses.append(fitness)
for genome, fitness in zip(NEAT.GetGenomeList(pop), fitnesses):
genome.SetFitness(fitness)
maxf = max([x.GetFitness() for x in NEAT.GetGenomeList(pop)])
print('Generation: {}, max fitness: {}'.format(generation, maxf))
if maxf > maxf_ever:
maxf_ever = maxf
hof.append(pickle.dumps(pop.GetBestGenome()))
pop.Epoch()
except KeyboardInterrupt:
pass
print('Replaying forever..')
if hof:
while True:
net = NEAT.NeuralNetwork()
g = pickle.loads(hof[-1])
g.BuildPhenotype(net)
do_trial(env, net, True)
import gym import time import MultiNEAT as NEAT import MultiNEAT.viz as viz import random as rnd import pickle import numpy as np import cv2 rng = NEAT.RNG() rng.TimeSeed() params = NEAT.Parameters() params.PopulationSize = 150 params.DynamicCompatibility = True params.WeightDiffCoeff = 1.0 params.CompatTreshold = 2.0 params.YoungAgeTreshold = 15 params.SpeciesMaxStagnation = 15 params.OldAgeTreshold = 35 params.MinSpecies = 2 params.MaxSpecies = 4 params.RouletteWheelSelection = False params.Elitism = True params.RecurrentProb = 0.15 params.OverallMutationRate = 0.2 params.MutateWeightsProb = 0.8 params.MutateNeuronTimeConstantsProb = 0.1
def main():
pygame.init()
screen = pygame.display.set_mode((600, 600))
pygame.display.set_caption("NEAT volleyball")
### Physics stuff
space = pm.Space()
space.gravity = Vec2d(0.0, -500.0)
# walls - the left-top-right walls
body = pm.Body()
walls= [pm.Segment(body, (50, 50), (50, 1550), 10)
,pm.Segment(body, (50, 1550), (560, 1550), 10)
,pm.Segment(body, (560, 1550), (560, 50), 10)
]
floor = pm.Segment(body, (50, 50), (560, 50), 10)
floor.friction = 1.0
floor.elasticity = 0.0
floor.collision_type = collision_type_floor
for s in walls:
s.friction = 0
s.elasticity = 0.99
s.collision_type = collision_type_wall
space.add(walls)
space.add(floor)
params = NEAT.Parameters()
params.PopulationSize = 250
params.DynamicCompatibility = True
params.AllowClones = True
params.CompatTreshold = 5.0
params.CompatTresholdModifier = 0.3
params.YoungAgeTreshold = 15
params.SpeciesMaxStagnation = 100
params.OldAgeTreshold = 35
params.MinSpecies = 3
params.MaxSpecies = 10
params.RouletteWheelSelection = True
params.RecurrentProb = 0.25
params.OverallMutationRate = 0.33
params.MutateWeightsProb = 0.90
params.WeightMutationMaxPower = 1.0
params.WeightReplacementMaxPower = 5.0
params.MutateWeightsSevereProb = 0.5
params.WeightMutationRate = 0.75
params.MaxWeight = 20
params.MutateAddNeuronProb = 0.01
params.MutateAddLinkProb = 0.05
params.MutateRemLinkProb = 0.00
rng = NEAT.RNG()
rng.TimeSeed()
#rng.Seed(0)
g = NEAT.Genome(0, 6, 0, 2, False, NEAT.ActivationFunction.TANH, NEAT.ActivationFunction.UNSIGNED_SIGMOID, 0, params)
pop = NEAT.Population(g, params, True, 1.0)
best_genome_ever = None
fast_mode = False
for generation in range(1000):
print "Generation:", generation
now = time.time()
genome_list = []
for s in pop.Species:
for i in s.Individuals:
genome_list.append(i)
print 'All individuals:', len(genome_list)
for i, g in enumerate(genome_list):
total_fitness = 0
for trial in range(20):
f, fast_mode = evaluate(g, space, screen, fast_mode, rnd.randint(80, 400), rnd.randint(-200, 200), rnd.randint(80, 400))
total_fitness += f
g.SetFitness(total_fitness / 20)
print
best = max([x.GetLeader().GetFitness() for x in pop.Species])
print 'Best fitness:', best, 'Species:', len(pop.Species)
# Draw the best genome's phenotype
net = NEAT.NeuralNetwork()
best_genome_ever = pop.Species[0].GetLeader()
best_genome_ever.BuildPhenotype(net)
img = np.zeros((250, 250, 3), dtype=np.uint8)
img += 10
NEAT.DrawPhenotype(img, (0, 0, 250, 250), net )
cv2.imshow("current best", img)
cv2.waitKey(1)
if best >= 10000:
break # evolution is complete if an individual keeps the ball up for that many timesteps
#if pop.GetStagnation() > 500:
# break
print "Evaluation took", time.time() - now, "seconds."
print "Reproducing.."
now = time.time()
pop.Epoch()
print "Reproduction took", time.time() - now, "seconds."
# Show the best genome's performance forever
pygame.display.set_caption("Best genome ever")
while True:
evaluate(best_genome_ever, space, screen, False, rnd.randint(80, 400), rnd.randint(-200, 200), rnd.randint(80, 400))
