def main():
""" Main function. """
pool = Pool.spawn(Genome.open(NN_STRUCTURE_FILE), 20, std=1)
# Set evolutionary parameters
eonn.samplesize = 5 # Sample size used for tournament selection
eonn.keep = 5 # Nr. of organisms copied to the next generation (elitism)
eonn.mutate_prob = 0.75 # Probability that offspring is being mutated
eonn.mutate_frac = 0.2 # Fraction of genes that get mutated
eonn.mutate_std = 0.1 # Std. dev. of mutation distribution (gaussian)
eonn.mutate_repl = 0.25 # Probability that a gene gets replaced
directory = "pics/" + ''.join(rand.sample(letters + digits, 5))
os.makedirs(directory)
# Evolve population
for j in xrange(1, ROUNDS + 1):
pool = eonn.optimize(pool, cliff, epochs=EPOCHS, evals=EVALS)
print "AFTER EPOCH", j * EPOCHS
print "average fitness %.1f" % pool.fitness
champion = max(pool)
print "champion fitness %.1f" % champion.fitness
for i in xrange(10):
cliff(champion.policy, verbose=True)
plt.savefig(directory + "/" + str(j * EPOCHS) + ".png")
plt.clf()
with open(directory + '/best.net', 'w') as f:
f.write('%s' % champion.genome)
print "Done, everything saved in ", directory
def evolve(heli, genome, popsize=50, epochs=100, keep=49, mutate_prob=.75, mutate_frac=.1, mutate_std=.8, mutate_repl=.25, verbose=False): """ Evolve a specialized policy for the given helicopter environment. """ # Set evolutionary parameters eonn.keep = keep eonn.mutate_prob = mutate_prob eonn.mutate_frac = mutate_frac eonn.mutate_std = mutate_std eonn.mutate_repl = mutate_repl # Evolve population and return champion feval = Evaluator(heli) pool = spawn(genome, popsize) return max(eonn.optimize(pool, feval.call, epochs, 1, verbose))
def main():
""" Main function. """
pool = Pool.spawn(Genome.open('mc.net'), 20, std=5.0)
# Set evolutionary parameters
eonn.KEEP = 5
eonn.MUTATE_PROB = 0.9
eonn.MUTATE_FRAC = 0.2
eonn.MUTATE_STD = 8.0
eonn.MUTATE_REPL = 0.1
# Evolve population
pool = eonn.optimize(pool, mc)
champion = max(pool)
# Print results
print '\ntrace:'
mc(champion.policy, verbose=True)
print '\ngenome:\n%s' % champion.genome
def main():
""" Main function. """
pool = Pool.spawn(Genome.open('mc.net'), 20, std=5.0)
# Set evolutionary parameters
eonn.keep = 5
eonn.mutate_prob = 0.9
eonn.mutate_frac = 0.2
eonn.mutate_std = 8.0
eonn.mutate_repl = 0.1
# Evolve population
pool = eonn.optimize(pool, mc)
champion = max(pool)
# Print results
print '\ntrace:'
mc(champion.policy, verbose=True)
print '\ngenome:\n%s' % champion.genome
def evolve(heli,
genome,
popsize=50,
epochs=100,
keep=49,
mutate_prob=.75,
mutate_frac=.1,
mutate_std=.8,
mutate_repl=.25,
verbose=False):
""" Evolve a specialized policy for the given helicopter environment. """
# Set evolutionary parameters
eonn.keep = keep
eonn.mutate_prob = mutate_prob
eonn.mutate_frac = mutate_frac
eonn.mutate_std = mutate_std
eonn.mutate_repl = mutate_repl
# Evolve population and return champion
feval = Evaluator(heli)
pool = spawn(genome, popsize)
return max(eonn.optimize(pool, feval.call, epochs, 1, verbose))
from eonn.genome import Genome
from eonn.organism import Pool
from helicopter.helicopter import Helicopter, XcellTempest
def hover(policy):
""" Helicopter evaluation function. """
state, sum_error = heli.reset()
while not heli.terminal:
action = policy.propagate(state, 1)
state, error = heli.update(action)
sum_error += error
return 1 / math.log(sum_error)
if __name__ == '__main__':
heli = Helicopter(XcellTempest.params, XcellTempest.noise_std)
pool = Pool.spawn(Genome.open('baseline.net'), 20)
# Set evolutionary parameters
eonn.keep = 15
eonn.mutate_prob = 0.9
eonn.mutate_frac = 0.1
eonn.mutate_std = 0.8
eonn.mutate_repl = 0.15
# Evolve population
pool = eonn.optimize(pool, hover)
champion = max(pool)
# Print results
print '\nerror:', math.exp(1 / hover(champion.policy))
print '\ngenome:\n%s' % champion.genome
from eonn.organism import Pool
def xor(policy, verbose=False):
""" XOR evaluation function. """
err = 0.0
input = [(i, j) for i in range(2) for j in range(2)]
for i in input:
output = policy.propagate(i, 1);
err += (output[0] - (i[0] ^ i[1]))**2
if verbose:
print i, '-> %.4f (%d)' % (output[0], round(output[0]))
return 1.0 / err
if __name__ == '__main__':
pool = Pool.spawn(Genome.open('xor.net'), 30)
# Set evolutionary parameters
eonn.KEEP = 1
eonn.MUTATE_PROB = 0.9
eonn.MUTATE_FRAC = 0.25
eonn.MUTATE_STD = 0.8
eonn.MUTATE_REPL = 0.2
# Evolve population
pool = eonn.optimize(pool, xor)
champion = max(pool)
# Print results
print '\noutput:'
xor(champion.policy, True)
print '\ngenome:\n%s' % champion.genome
from eonn.genome import Genome
from eonn.organism import Pool
from helicopter.helicopter import Helicopter, XcellTempest
def hover(policy):
""" Helicopter evaluation function. """
state, sum_error = heli.reset()
while not heli.terminal:
action = policy.propagate(state, 1)
state, error = heli.update(action)
sum_error += error
return 1 / math.log(sum_error)
if __name__ == '__main__':
heli = Helicopter(XcellTempest.params, XcellTempest.noise_std)
pool = Pool.spawn(Genome.open('baseline.net'), 20)
# Set evolutionary parameters
eonn.keep = 15
eonn.mutate_prob = 0.9
eonn.mutate_frac = 0.1
eonn.mutate_std = 0.8
eonn.mutate_repl = 0.15
# Evolve population
pool = eonn.optimize(pool, hover)
champion = max(pool)
# Print results
print '\nerror:', math.exp(1 / hover(champion.policy))
print '\ngenome:\n%s' % champion.genome
from eonn.organism import Pool
def xor(policy, verbose=False):
""" XOR evaluation function. """
err = 0.0
input = [(i, j) for i in range(2) for j in range(2)]
for i in input:
output = policy.propagate(i, 1);
err += (output[0] - (i[0] ^ i[1]))**2
if verbose:
print i, '-> %.4f' % output[0]
return 1.0 / err
if __name__ == '__main__':
pool = Pool.spawn(Genome.open('xor.net'), 30)
# Set evolutionary parameters
eonn.keep = 1
eonn.mutate_prob = 0.9
eonn.mutate_frac = 0.25
eonn.mutate_std = 0.8
eonn.mutate_repl = 0.2
# Evolve population
pool = eonn.optimize(pool, xor)
champion = max(pool)
# Print results
print '\noutput:'
xor(champion.policy, True)
print '\ngenome:\n%s' % champion.genome
