import random
import pyrosim
import matplotlib.pyplot as plt
from robot import ROBOT
from individual import INDIVIDUAL
for i in range(0, 10):
individual = INDIVIDUAL()
individual.evaluate()
# sim = pyrosim.Simulator(eval_time=500, play_paused=False)
#
# robot = ROBOT(sim, random.random()*2-1)
#
# # Start
#
# sim.start()
#
# sim.wait_to_finish()
#
# # y
# sensorData = sim.get_sensor_data(sensor_id=robot.P4, svi=1)
#
# print(sensorData[-1])
#
# # Analyze sensor data
#
# f = plt.figure()
#
from individual import INDIVIDUAL
'''
Instructions:
Implement hill climber search
https://www.reddit.com/r/ludobots/wiki/pyrosim/hillclimber
Blue diamond is the y coordinate
'''
plot_flag = True
visualize_best = True
fitness_robots = []
parent = INDIVIDUAL()
parent.evaluate(blind_mode=False)
print(parent.fitness)
for i in range(100):
child = copy.deepcopy(parent)
child.mutate()
child.evaluate(blind_mode=True)
print('[g:{}] - [Pw:{}] - [P:{}] - [c:{}]'.format(i, parent.genome, parent.fitness, child.fitness))
if (child.fitness > parent.fitness):
parent = child
fitness_robots.append(child.fitness)
child.evaluate(blind_mode=True)
# Save the strongest model
f = open('robot.p', 'wb')
import matplotlib.pyplot as plt
import pickle
from robot import ROBOT
from individual import INDIVIDUAL
from copy import deepcopy
#create objects
parent = INDIVIDUAL()
print(parent.evaluate(play_blind=True))
for i in range(100):
child = deepcopy(parent)
child.mutate()
print(
f'[g: {i}] [pw: {parent.genome}] [p: {parent.fitness}] [c: {child.evaluate(play_blind=True)}]'
)
if (child.fitness > parent.fitness):
parent = child
# f = open('robot.p','wb')
# pickle.dump(parent , f )
# f.close()
# child.evaluate()
import random
import pyrosim
import matplotlib.pyplot as plt
from robot import ROBOT
import copy
import pickle
from individual import INDIVIDUAL
parent = INDIVIDUAL()
parent.evaluate(True)
for i in range(0, 100):
child = copy.deepcopy(parent)
child.mutate()
child.evaluate(True)
print("[g: %s] [pw: %s] [p: %s] [c: %s]" %
(i + 1, parent.genome, parent.fitness, child.fitness))
if child.fitness > parent.fitness:
child.evaluate(True)
parent = child
# f = open("robot.p", "wb")
#
# pickle.dump(parent, f)
#
# f.close()
