def make_movie(net, force_function, duration_seconds, output_filename):
w, h = 300, 100
scale = 300 / 6
cart = gz.rectangle(scale * 0.5, scale * 0.25, xy=(150, 80), stroke_width=1, fill=(0, 1, 0))
pole = gz.rectangle(scale * 0.1, scale * 1.0, xy=(150, 55), stroke_width=1, fill=(1, 1, 0))
sim = CartPole()
def make_frame(t):
inputs = sim.get_scaled_state()
if hasattr(net, 'activate'):
action = net.activate(inputs)
else:
action = net.advance(inputs, sim.time_step, sim.time_step)
sim.step(force_function(action))
surface = gz.Surface(w, h, bg_color=(1, 1, 1))
# Convert position to display units
visX = scale * sim.x
# Draw cart.
group = gz.Group((cart,)).translate((visX, 0))
group.draw(surface)
# Draw pole.
group = gz.Group((pole,)).translate((visX, 0)).rotate(sim.theta, center=(150 + visX, 80))
group.draw(surface)
return surface.get_npimage()
clip = mpy.VideoClip(make_frame, duration=duration_seconds)
clip.write_videofile(output_filename, codec="mpeg4", fps=50)
def evaluate_population(genomes, create_func, force_func):
for g in genomes:
net = create_func(g)
fitness = 0
for runs in range(runs_per_net):
sim = CartPole()
fitness += run_simulation(sim, net, force_func)
# The genome's fitness is its average performance across all runs.
g.fitness = fitness / float(runs_per_net)
NO_LEARNING_THRESHOLD = 20 # Time cycle of the simulation time = 0 # These variables perform bookkeeping (how many cycles was the pole # balanced for before it fell). Useful for plotting learning curves. time_steps_to_failure = [] num_failures = 0 time_at_start_of_current_trial = 0 # You should reach convergence well before this max_failures = 500 # Initialize a cart pole cart_pole = CartPole(Physics()) # Starting `state_tuple` is (0, 0, 0, 0) # x, x_dot, theta, theta_dot represents the actual continuous state vector x, x_dot, theta, theta_dot = 0.0, 0.0, 0.0, 0.0 state_tuple = (x, x_dot, theta, theta_dot) # `state` is the number given to this state, you only need to consider # this representation of the state state = cart_pole.get_state(state_tuple) # if min_trial_length_to_start_display == 0 or display_started == 1: # cart_pole.show_cart(state_tuple, pause_time) # Perform all your initializations here: # Assume no transitions or rewards have been observed. # Initialize the value function array to small random values (0 to 0.10,
# load the winner
with open('winner-ctrnn', 'rb') as f:
c = pickle.load(f)
print('Loaded genome:')
print(c)
# Load the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'config-ctrnn')
config = neatfast.Config(neatfast.DefaultGenome, neatfast.DefaultReproduction,
neatfast.DefaultSpeciesSet, neatfast.DefaultStagnation,
config_path)
sim = CartPole()
net = neatfast.ctrnn.CTRNN.create(c, config, sim.time_step)
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 120 seconds.
balance_time = 0.0
while sim.t < 120.0:
inputs = sim.get_scaled_state()
mpc = MPC(0.5, 0, 0, 0)
ports = list(serial.tools.list_ports.comports())
print(ports)
for p in ports:
print(p)
if p[2] == "USB VID:PID=268b:0201 SNR=160045F45953":
sabre_port = p[0]
elif p[2] == "USB VID:PID=2341:0043 SNR=75334323935351D0D022":
ard_port = p[0]
motor = SabreControl(port=sabre_port)
encoder = EncoderAnalyzer(port=ard_port)
#image = ImageAnalyzer(0,show_image=True)
cart = CartPole(motor, encoder, encoder, encoder)
cart.zeroAngleAnalyzer()
#encoder.setAngleZero()
cart.zeroPosAnalyzer()
cart.goTo(.6)
fig = plt.figure()
plt.autoscale(tight=True)
ax = fig.add_subplot(111)
ax.autoscale(enable=True, axis="y", tight=False)
li_x = None
command_speed = 0
last_time = time.time()
def evaluate_population(population):
simulation = CartPole(population, markov = False)
# comment this line to print the status
simulation.print_status = False
simulation.run()
from numpy import pi
from cart_pole import CartPole
from plot_graphs import visualize_plots
from render_movie import visualize_movie
from learning_system import LearningCorrection
play_movie = True
ideal_cart_pole = CartPole(
dt=.001,
init_conds=[0, 0., pi, 0.], #x, dx, phi, dphi
end=10,
)
ideal_cart_pole.compute_lqr_gain()
ideal_data = ideal_cart_pole.integrate()
dist_cart_pole = CartPole(
dt=.001,
init_conds=[0, 0., pi, 0.], #x, dx, phi, dphi
end=10,
disturb=False,
)
dist_cart_pole.compute_lqr_gain()
dist_data = dist_cart_pole.integrate()
# ## Learn the system model
lc = LearningCorrection(ideal_system=ideal_cart_pole,
ideal_data=ideal_data,
real_system=dist_cart_pole)
with open('winner-feedforward', 'rb') as f:
c = pickle.load(f)
print('Loaded genome:')
print(c)
# Load the config file, which is assumed to live in
# the same directory as this script.
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir, 'config-feedforward-cartpole')
config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
neat.DefaultSpeciesSet, neat.DefaultStagnation,
config_path)
net = neat.nn.FeedForwardNetwork.create(c, config)
sim = CartPole(x=2.0, dx=0, dtheta=0, theta=-30.0*math.pi/180.0)
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 120 seconds.
balance_time = 0.0
while sim.t < 120.0:
inputs = sim.get_scaled_state()
action = net.activate(inputs)
from cart_pole import CartPole
import serial.tools.list_ports
ports = list(serial.tools.list_ports.comports())
print(ports)
for p in ports:
print(p)
if p[2] == "USB VID:PID=268b:0201 SNR=160045F45953":
sabre_port = p[0]
elif p[2] == "USB VID:PID=2341:0043 SNR=75334323935351D0D022":
ard_port = p[0]
motor = SabreControl(port=sabre_port)
encoder = EncoderAnalyzer(port=ard_port)
image = ImageAnalyzer(0, show_image=True)
cart = CartPole(motor, encoder, image, encoder)
cart.zeroAngleAnalyzer()
#encoder.setAngleZero()
cart.zeroPosAnalyzer()
cart.goTo(.5)
gravity = 9.8
mass_pole = 0.1
length = 0.5
moment_of_inertia = (1. / 3.) * mass_pole * length**2
print(moment_of_inertia)
A = np.array(
[[0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1],
raise Exception('''This example is currently broken, it will be fixed soon. In the meantime,
try the single_pole or xor examples.''')
import os
import sys
import cPickle
from cart_pole import CartPole
from neatsociety.config import Config
filename = 'winner_chromosome'
if len(sys.argv) > 1:
filename = sys.argv[1]
# load genome
print("loading genome {0!s}".format(filename))
with open(filename) as f:
c = cPickle.load(f)
# load settings file
local_dir = os.path.dirname(__file__)
config = Config(os.path.join(local_dir, 'dpole_config'))
print("Loaded genome:\n{0!s}".format(c))
# starts the simulation
simulator = CartPole([c], markov=False)
simulator.run(testing=True)
