def evaluate_genome(g):
net = ctrnn.create_phenotype(g)
fitnesses = []
for runs in range(runs_per_net):
sim = cart_pole.CartPole()
# Run the given simulation for up to num_steps time steps.
fitness = 0.0
for s in range(num_steps):
inputs = sim.get_scaled_state()
action = net.parallel_activate(inputs)
# Apply action to the simulated cart-pole
force = cart_pole.discrete_actuator_force(action)
sim.step(force)
# Stop if the network fails to keep the cart within the position or angle limits.
# The per-run fitness is the number of time steps the network can balance the pole
# without exceeding these limits.
if abs(sim.x) >= sim.position_limit or abs(
sim.theta) >= sim.angle_limit_radians:
break
fitness += 1.0
fitnesses.append(fitness)
# The genome's fitness is its worst performance across all runs.
return min(fitnesses)
def evaluate_genome(g):
net = ctrnn.create_phenotype(g)
fitnesses = []
for runs in range(runs_per_net):
sim = cart_pole.CartPole()
# Run the given simulation for up to num_steps time steps.
fitness = 0.0
for s in range(num_steps):
inputs = sim.get_scaled_state()
action = net.parallel_activate(inputs)
# Apply action to the simulated cart-pole
force = cart_pole.discrete_actuator_force(action)
sim.step(force)
# Stop if the network fails to keep the cart within the position or angle limits.
# The per-run fitness is the number of time steps the network can balance the pole
# without exceeding these limits.
if abs(sim.x) >= sim.position_limit or abs(sim.theta) >= sim.angle_limit_radians:
break
fitness += 1.0
fitnesses.append(fitness)
# The genome's fitness is its worst performance across all runs.
return min(fitnesses)
def evaluate_genome(genomes):
for g in genomes:
net = ctrnn.create_phenotype(g)
fitness = 0.0
for t in test_values:
net.reset()
output = net.serial_activate([t])
expected = t ** 2
error = output[0] - expected
fitness -= error ** 2
g.fitness = fitness
def evaluate_population(population):
twelve_degrees = 0.2094384 #radians
num_steps = 10**5
for chromo in population:
net = ctrnn.create_phenotype(chromo)
# old way (harder!)
#x = random.uniform(-2.4, 2.4) # cart position, meters
#x_dot = random.uniform(-1.0, 1.0) # cart velocity
#theta = random.uniform(-0.2, 0.2) # pole angle, radians
#theta_dot = random.uniform(-1.5, 1.5) # pole angular velocity
# initial conditions (as used by Stanley)
x = (random.randint(0, 2**31)%4800)/1000.0 - 2.4
x_dot = (random.randint(0, 2**31)%2000)/1000.0 - 1;
theta = (random.randint(0, 2**31)%400)/1000.0 - .2
theta_dot = (random.randint(0, 2**31)%3000)/1000.0 - 1.5
#x = 0.0
#x_dot = 0.0
#theta = 0.0
#theta_dot = 0.0
fitness = 0
for trials in xrange(num_steps):
# maps into [0,1]
inputs = [(x + 2.4)/4.8,
(x_dot + 0.75)/1.5,
(theta + twelve_degrees)/0.41,
(theta_dot + 1.0)/2.0]
action = net.pactivate(inputs)
# Apply action to the simulated cart-pole
x, x_dot, theta, theta_dot = cart_pole(action[0], x, x_dot, theta, theta_dot)
# Check for failure. If so, return steps
# the number of steps indicates the fitness: higher = better
fitness += 1
if (abs(x) >= 2.5 or abs(theta) >= twelve_degrees):
#if abs(theta) > twelve_degrees: # Igel (p. 5)
# the cart/pole has run/inclined out of the limits
break
chromo.fitness = fitness
def evaluate_population(population):
twelve_degrees = 0.2094384 #radians
num_steps = 10**5
for chromo in population:
net = ctrnn.create_phenotype(chromo)
# old way (harder!)
#x = random.uniform(-2.4, 2.4) # cart position, meters
#x_dot = random.uniform(-1.0, 1.0) # cart velocity
#theta = random.uniform(-0.2, 0.2) # pole angle, radians
#theta_dot = random.uniform(-1.5, 1.5) # pole angular velocity
# initial conditions (as used by Stanley)
x = (random.randint(0, 2**31) % 4800) / 1000.0 - 2.4
x_dot = (random.randint(0, 2**31) % 2000) / 1000.0 - 1
theta = (random.randint(0, 2**31) % 400) / 1000.0 - .2
theta_dot = (random.randint(0, 2**31) % 3000) / 1000.0 - 1.5
#x = 0.0
#x_dot = 0.0
#theta = 0.0
#theta_dot = 0.0
fitness = 0
for trials in xrange(num_steps):
# maps into [0,1]
inputs = [(x + 2.4) / 4.8, (x_dot + 0.75) / 1.5,
(theta + twelve_degrees) / 0.41, (theta_dot + 1.0) / 2.0]
action = net.pactivate(inputs)
# Apply action to the simulated cart-pole
x, x_dot, theta, theta_dot = cart_pole(action[0], x, x_dot, theta,
theta_dot)
# Check for failure. If so, return steps
# the number of steps indicates the fitness: higher = better
fitness += 1
if (abs(x) >= 2.5 or abs(theta) >= twelve_degrees):
#if abs(theta) > twelve_degrees: # Igel (p. 5)
# the cart/pole has run/inclined out of the limits
break
chromo.fitness = fitness
import pickle
from cart_pole import CartPole, discrete_actuator_force
from movie import make_movie
from neat import ctrnn
# load the winner
with open('ctrnn_winner_genome', 'rb') as f:
c = pickle.load(f)
print('Loaded genome:')
print(c)
net = ctrnn.create_phenotype(c)
sim = CartPole()
print()
print("Initial conditions:")
print(" x = {0:.4f}".format(sim.x))
print(" x_dot = {0:.4f}".format(sim.dx))
print(" theta = {0:.4f}".format(sim.theta))
print("theta_dot = {0:.4f}".format(sim.dtheta))
print()
# Run the given simulation for up to 100k time steps.
num_balanced = 0
for s in range(10 ** 5):
inputs = sim.get_scaled_state()
action = net.parallel_activate(inputs)
import pickle
from cart_pole import CartPole, discrete_actuator_force
from movie import make_movie
from neat import ctrnn
# load the winner
with open('ctrnn_winner_genome', 'rb') as f:
c = pickle.load(f)
print('Loaded genome:')
print(c)
net = ctrnn.create_phenotype(c)
sim = CartPole()
print()
print("Initial conditions:")
print(" x = {0:.4f}".format(sim.x))
print(" x_dot = {0:.4f}".format(sim.dx))
print(" theta = {0:.4f}".format(sim.theta))
print("theta_dot = {0:.4f}".format(sim.dtheta))
print()
# Run the given simulation for up to 100k time steps.
num_balanced = 0
for s in range(10**5):
inputs = sim.get_scaled_state()
action = net.parallel_activate(inputs)
