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)