if EVO_MODE == 1:
print("evolving for robustness")
else:
print("evolving for maximum fitness")
def GetNewController():
return np.random.choice(controllerTypes)
aggregates = FIXEDAGGPOP(AGGREGATE, num_cubes=num_cubes)
controllers = POPULATION(GetNewController(), pop_size=N, unique=True)
aggregates.initialize()
controllers.initialize()
coevolve = COEVOLVE(aggregates, controllers, EVO_MODE, seed)
latestGen = 1
if os.path.exists("./saved_generations/gen%d.p" % latestGen):
while os.path.exists("./saved_generations/gen%d.p" % latestGen):
print("Gen", latestGen, "exists")
latestGen += 1
f = open("./saved_generations/gen%d.p" % (latestGen - 1), 'rb')
saveState = pickle.load(f)
coevolve = saveState[0]
seed = saveState[1]
np.random.set_state(seed)
f.close()
logging.basicConfig(level=logging.INFO)
import pickle
import pyrosim
import matplotlib.pyplot as plt
import numpy as np
from robot import ROBOT
from individual import INDIVIDUAL
from population import POPULATION
import constants
from environments import ENVIRONMENTS
# create environment with "light source"
envs = ENVIRONMENTS(numEnvs=constants.numEnvs, eval_time=constants.eval_time)
# create initial population
parents = POPULATION(envs,
popSize=constants.popSize,
eval_time=constants.eval_time)
parents.initialize()
parents.evaluate(play_blind=True, play_paused=False)
print(0, parents)
# evolve:
for g in range(constants.numGen):
children = POPULATION(envs, parents=parents)
children.fillFrom(parents)
children.evaluate(play_blind=True, play_paused=False)
parents.replaceWith(children)
print(g + 1, parents)
parents.playbest()
