def make_frame(t):
global x, x_dot, theta, theta_dot, last_t
# maps into [0,1]
inputs = [(x + 2.4) / 4.8,
(x_dot + 0.75) / 1.5,
(theta + angle_limit) / 0.41,
(theta_dot + 1.0) / 2.0]
action = net.pactivate(inputs)
action[0] += 0.4 * (np.random.random() - 0.5)
# Apply action to the simulated cart-pole
x, x_dot, theta, theta_dot = cart_pole(action[0], x, x_dot, theta, theta_dot)
surface = gz.Surface(W, H, bg_color=(1, 1, 1))
# Convert position to display units
visX = SCALE * x
# Draw cart.
group = gz.Group((cart,)).translate((visX, 0))
group.draw(surface)
# Draw pole.
group = gz.Group((pole,)).translate((visX, 0)).rotate(theta, center=(150 + visX, 80))
group.draw(surface)
return surface.get_npimage()
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'spole_config'))
net = nn.create_phenotype(c)
# initial conditions (as used by Stanley)
x = randint(0, 4799) / 1000.0 - 2.4
x_dot = randint(0, 1999) / 1000.0 - 1.0
theta = randint(0, 399) / 1000.0 - 0.2
theta_dot = randint(0, 2999) / 1000.0 - 1.5
print "\nInitial conditions:"
print "%2.4f %2.4f %2.4f %2.4f" % (x, x_dot, theta, theta_dot)
for step in xrange(10 ** 5):
# maps into [0,1]
inputs = [(x + 2.4) / 4.8,
(x_dot + 0.75) / 1.5,
(theta + angle_limit) / 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)
if abs(x) >= 2.4 or abs(theta) >= angle_limit:
print '\nFailed at step %d \n' % step
sys.exit(1)
print '\nPole balanced for 10^5 time steps!'