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,
def evaluate_population(pop):
simulation = CartPole(pop, markov=False)
# comment this line to print the status
simulation.print_status = False
simulation.run()
import pickle
from cart_pole import CartPole, discrete_actuator_force
from movie import make_movie
from neat import nn
# load the winner
with open('nn_winner_genome', 'rb') as f:
c = pickle.load(f)
print('Loaded genome:')
print(c)
net = nn.create_feed_forward_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.serial_activate(inputs)
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()
# 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()
from movie import make_movie
import neat
from neat import nn
# load the winner
with open("my_network.data", 'rb') as fd:
winner, config, stats = pickle.load(fd)
print('Loaded genome:')
print(winner)
winner_net = neat.nn.FeedForwardNetworkFPGA.create(winner, config)
#winner_net = neat.nn.FeedForwardNetwork.create(winner, config)
#winner_net.my_create_net_layer(winner, config)
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 120 seconds.
balance_time = 0.0
# while sim.t < 120.0:
# inputs = sim.get_scaled_state()
# action = winner_net.activate(inputs)
# #action = winner_net.my_activate(inputs)
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,
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)
def evaluate_population(population):
simulation = CartPole(population, markov = False)
# comment this line to print the status
simulation.print_status = False
simulation.run()
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)
import cPickle as pickle
from cart_pole import CartPole
if len(sys.argv) > 1:
# load genome
try:
file = open(sys.argv[1], 'r')
except IOError:
print "Filename: '"+sys.argv[1]+"' not found!"
sys.exit(0)
else:
c = pickle.load(file)
file.close()
else:
print "Loading default winner chromosome file"
try:
file = open('winner_chromosome', 'r')
except IOError:
print "Winner chromosome not found!"
sys.exit(0)
else:
c = pickle.load(file)
file.close()
# load settings file
config.load('cpExp_config')
print "Loaded genome:"
print c
# starts the simulation
simulator = CartPole([c], markov=False)
simulator.run(testing=True)
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 = 9.81
mass_pole = 0.15
length = 0.5
moment_of_inertia = (1. / 3.) * mass_pole * length**2
def E(x): # energy
return (moment_of_inertia * x[3]**2 / 2) - (np.cos(x[2]) * length *
mass_pole * gravity / 2)
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)
from analyzer import EncoderAnalyzer, ImageAnalyzer
from motor_control import SabreControl
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)
while True:
#print('image' , "%02.2f"%cart.angle_analyzer.getAngle(), 'encoder', "%02.2f"%cart.pos_analyzer.getAngle())
#print('image' , "%02.2f"%cart.angle_analyzer.getAngle(),
# "%02.2f"%cart.angle_analyzer.getAngleVel(),
# 'encoder' , "%02.2f"%cart.pos_analyzer.getAngle(),
# "%02.2f"%cart.pos_analyzer.getAngleVel())
print(cart.getState())
