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)
import pickle
from cart_pole import CartPole, discrete_actuator_force
from movie import make_movie
from neatsociety 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 neatsociety 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)
