type=str,
default="default",
help="architecture's name")
args = parser.parse_args()
agent_position, agent_velocity, leader_position, leader_velocity, \
atk_arch, def_arch, train_par, test_par, \
robustness_formula = get_settings(args.architecture, mode="test")
pg = ParametersHyperparallelepiped(agent_position, agent_velocity,
leader_position, leader_velocity)
physical_model = model_platooning.Model(pg.sample(sigma=0.05))
attacker = architecture.Attacker(physical_model, *atk_arch.values())
defender = architecture.Defender(physical_model, *def_arch.values())
relpath = get_relpath(main_dir="platooning_" + args.architecture,
train_params=train_par)
load_models(attacker, defender, EXP + relpath)
def run(mode=None):
physical_model.initialize_random()
conf_init = {
'x': physical_model.agent.x,
'dot_x': physical_model.agent.dot_x,
'theta': physical_model.agent.theta,
'dot_theta': physical_model.agent.dot_theta,
}
dest="nfollowers",
type=int,
default=1,
help="number of followers")
parser.add_argument("--dark",
default=False,
action="store_true",
help="Use dark theme")
args = parser.parse_args()
if args.dark:
plt.style.use('./qb-common_dark.mplstyle')
physical_model = model_platooning.Model(None)
attacker = architecture.Attacker(physical_model, 2, 10, 2)
defender = architecture.Defender(physical_model, 2, 10)
misc.load_models(attacker, defender, args.dirname)
dt = 0.05
steps = 300
def run(n_followers=1, mode=None):
init_dist = random.uniform(1, 8)
init_vel = random.uniform(1, 5)
print(mode, 'd:', init_dist, 'v:', init_vel)
leader = Leader(float(n_followers * init_dist), init_vel)
followers = [
Follower(init_dist * (float(n_followers) - i - 1), init_vel)