def coevo():
# Create a pool for the policies
pi_pool = Pool.spawn(Genome.open(NN_STRUCTURE_FILE), 20, std=8)
# Create a pool of z's, starting around [0.5,0.5], should probably be better
z_list = [[x] for x in np.linspace(0, 0.5, 5)]
genomes = BasicGenome.from_list(z_list, 5)
org_list = [Organism(genome) for genome in genomes]
z_pool = Pool(org_list)
avg_fitness = []
champ_fitness = []
for i in xrange(150):
pi_pool = eonn.epoch(pi_pool, len(pi_pool))
z_pool = eonn.epoch(z_pool, len(z_pool))
for pi_org, z_org in itertools.product(pi_pool, z_pool):
reward = cliff(pi_org.genome, z=[z_org.weights[0]], verbose=False)
pi_org.evals.append(reward)
z_org.evals.append(reward)
for org in z_pool:
org.evals = [np.var(org.evals)]
avg_fitness.append(pi_pool.fitness)
champion = max(pi_pool)
champ_fitness.append(champion.fitness)
return avg_fitness, champ_fitness
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 initGP():
"""Do simulations with random pi,z and create GP, X, y"""
poolsize = 68
pool = Pool.spawn(Genome.open(NN_STRUCTURE_FILE), poolsize, std=10)
X = []
for i, org in enumerate(pool):
org.mutate()
genome = org.genome
w = genome.weights
z = [np.random.uniform(0, 0.3)]
reward = cliff(genome, z)
while reward <= 0 and len(X) < poolsize / 2:
#Train input policies to reach the goal.
org.mutate()
genome = org.genome
w = genome.weights
reward = cliff(genome, z)
if not len(X):
X = np.atleast_2d(w + z)
y = np.atleast_2d([reward])
else:
X = np.append(X, [w + z], axis=0)
y = np.append(y, [reward])
# Initialize GP with kernel parameters.
GP = GaussianProcess(theta0=0.1, thetaL=.001, thetaU=1.)
GP.fit(X, y)
return GP, X, y
def learn_genomeIRL(self): #input the reward function
pool = Pool.spawn(Genome.open('policies/generic.net'), 20)
# Set evolutionary parameters
eonnIRL.keep = 15 ; eonnIRL.mutate_prob = 0.4 ; eonnIRL.mutate_frac = 0.1;eonnIRL.mutate_std = 0.8;eonnIRL.mutate_repl = 0.15
# Evolve population
pool = eonnIRL.optimize(pool, self.percieved_eval,400) # These are imported functions from EONNIRL
champion = max(pool)
# Print results
print '\nerror:', math.exp(1 / self.percieved_eval(champion.policy))
#print '\ngenome:\n%s' % champion.genome
return champion.policy
def learnGenomeIRL(theta): #input the reward function
pool = Pool.spawn(Genome.open('policies/generic.net'), 20)
# Set evolutionary parameters
eonnIRL.keep = 15
eonnIRL.mutate_prob = 0.4
eonnIRL.mutate_frac = 0.1
eonnIRL.mutate_std = 0.8
eonnIRL.mutate_repl = 0.15
# Evolve population
pool = eonnIRL.optimize(pool, hoverIRL, theta)
champion = max(pool)
# Print results
print '\nerror:', math.exp(1 / hover(champion.policy))
print '\nerror:', math.exp(1 / hoverIRL(champion.policy, theta))
print '\ngenome:\n%s' % champion.genome
def learnGenomeIRL(theta): #input the reward function
pool = Pool.spawn(Genome.open('policies/generic.net'), 20)
# Set evolutionary parameters
eonnIRL.keep = 15
eonnIRL.mutate_prob = 0.4
eonnIRL.mutate_frac = 0.1
eonnIRL.mutate_std = 0.8
eonnIRL.mutate_repl = 0.15
# Evolve population
pool = eonnIRL.optimize(pool, hoverIRL,theta)
champion = max(pool)
# Print results
print '\nerror:', math.exp(1 / hover(champion.policy))
print '\nerror:', math.exp(1 / hoverIRL(champion.policy,theta))
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 find_best(GP, epochs=100):
""" Find the best policy in the GP """
pool = Pool.spawn(Genome.open(NN_STRUCTURE_FILE), 50, std=8)
all_z = list(np.linspace(0, max_wind, 10))
for n in xrange(epochs):
if n != 0:
pool = eonn.epoch(pool, len(pool))
weights = [np.append(org.weights, z) for org in pool for z in all_z]
reward = GP.predict(weights)
for i in xrange(len(pool)):
pool[i].evals = list(reward[i * len(all_z):(i + 1) * len(all_z)])
champion = max(pool)
return champion
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 find_best_upper(GP, epochs=100):
""" Find policy with highest upperbound """
pool = Pool.spawn(Genome.open(NN_STRUCTURE_FILE), 50, std=8)
all_z = list(np.linspace(0, max_wind, 10))
for n in xrange(epochs):
if n != 0:
pool = eonn.epoch(pool, len(pool))
weights = [np.append(org.weights, z) for org in pool for z in all_z]
reward, MSE = GP.predict(weights, eval_MSE=True)
reward += 1.96 * np.sqrt(MSE)
for i in xrange(len(pool)):
pool[i].evals = list(reward[i * len(all_z):(i + 1) * len(all_z)])
champion = max(pool)
return champion
def acquisition(GP, epochs):
"""
Select the best (pi,z)-pair to evaluate using GP and GA
"""
# Create a pool for the policies
pi_pool = Pool.spawn(Genome.open(NN_STRUCTURE_FILE), 20, std=8)
# Create a pool of z's, starting around [0.5,0.5], should probably be better
z_list = list(itertools.product(np.arange(0, max_wind, 1. / 20)))
genomes = BasicGenome.from_list(z_list, 20)
org_list = [Organism(genome) for genome in genomes]
z_pool = Pool(org_list)
for _ in xrange(epochs):
pi_pool, z_pool, x_predict, reward_predict, MSE = do_evolution(
pi_pool, z_pool, GP)
# get scores
reward_predictGrid = np.reshape(reward_predict,
(len(pi_pool), len(z_pool)))
ub = 1.96 * np.sqrt(MSE)
ub_predictGrid = np.reshape(ub, (len(pi_pool), len(z_pool)))
pi_score = score_pi(reward_predictGrid, ub_predictGrid)
z_score = score_z(reward_predictGrid, ub_predictGrid)
# add scores to organisms
add_pi_scores(pi_pool, x_predict, pi_score)
add_z_scores(z_pool, x_predict, z_score)
# return current best pi and z
pi_org = max(pi_pool)
z_org = max(z_pool)
return pi_org, z_org
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