import argparse
from environments import environments
from performer import Performer
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--environment',
default='RLBench',
help='Environment to use for training [default = RLBench]')
parser.add_argument(
'--load_model',
default='./model.model',
help='Path to load the model [default = [./model.model]')
parser.add_argument(
'--n_tests',
default=10,
type=int,
help='How many times to run the simulation [default = 10]')
args = parser.parse_args()
SIMULATOR, NETWORK = environments[args.environment]
model = NETWORK()
model.load(args.load_model)
performer = Performer(0, model, SIMULATOR)
performer.perform(args)
from naoqi import ALProxy
from configuration import nao_ip, nao_port, music_path, modules_list
from search import IterativeDeepening
import numpy as np
import time
from performer import Performer
mpm = 60/(136/4)
mandatory_position = [14, 17, 15, 18, 11, 13, 12, 1]
mandatory_times = [0, 14.12, 14.12, 28.24, 28.24, 28.24, 26.47, 15.88]
pool_times = np.loadtxt('times.txt')
search_alg = IterativeDeepening(pool_times, max_error=0.01, max_depth=5)
solution = search_alg.find_complete_path(mandatory_position, mandatory_times, mpm)
print(solution)
positions = [modules_list[i] for i in solution]
performer = Performer(nao_ip, nao_port, positions[0])
t1 = time.time()
performer.perform(positions[1:], music_path)
print("Tempo effettivo: ", time.time() - t1)
