def create_qqsu_setup():
# Environments
env_hparams = dict(dt=1 / 100.0, max_steps=600)
env_real = QQubeSwingUpSim(**env_hparams)
env_real.domain_param = dict(
mass_rot_pole=0.095 * 0.9, # 0.095*0.9 = 0.0855
mass_pend_pole=0.024 * 1.1, # 0.024*1.1 = 0.0264
length_rot_pole=0.085 * 0.9, # 0.085*0.9 = 0.0765
length_pend_pole=0.129 * 1.1, # 0.129*1.1 = 0.1419
)
env_sim = QQubeSwingUpSim(**env_hparams)
randomizer = DomainRandomizer(
NormalDomainParam(name="mass_rot_pole",
mean=0.0,
std=1e-9,
clip_lo=1e-3),
NormalDomainParam(name="mass_pend_pole",
mean=0.0,
std=1e-9,
clip_lo=1e-3),
NormalDomainParam(name="length_rot_pole",
mean=0.0,
std=1e-9,
clip_lo=1e-3),
NormalDomainParam(name="length_pend_pole",
mean=0.0,
std=1e-9,
clip_lo=1e-3),
)
env_sim = DomainRandWrapperLive(env_sim, randomizer)
dp_map = {
0: ("mass_rot_pole", "mean"),
1: ("mass_rot_pole", "std"),
2: ("mass_pend_pole", "mean"),
3: ("mass_pend_pole", "std"),
4: ("length_rot_pole", "mean"),
5: ("length_rot_pole", "std"),
6: ("length_pend_pole", "mean"),
7: ("length_pend_pole", "std"),
}
# trafo_mask = [False, True, False, True, False, True, False, True]
trafo_mask = [True] * 8
env_sim = MetaDomainRandWrapper(env_sim, dp_map)
# Policies (the behavioral policy needs to be deterministic)
behavior_policy = QQubeSwingUpAndBalanceCtrl(env_sim.spec)
prior = DomainRandomizer(
NormalDomainParam(name="mass_rot_pole", mean=0.095, std=0.095 / 10),
NormalDomainParam(name="mass_pend_pole", mean=0.024, std=0.024 / 10),
NormalDomainParam(name="length_rot_pole", mean=0.085, std=0.085 / 10),
NormalDomainParam(name="length_pend_pole", mean=0.129, std=0.129 / 10),
)
ddp_policy = DomainDistrParamPolicy(mapping=dp_map,
trafo_mask=trafo_mask,
prior=prior,
scale_params=False)
return env_sim, env_real, env_hparams, dp_map, behavior_policy, ddp_policy
def get_default_randomizer_pend() -> DomainRandomizer:
"""
Get the default randomizer for the `PendulumSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.pendulum import PendulumSim
dp_nom = PendulumSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='g',
mean=dp_nom['g'],
std=dp_nom['g'] / 10,
clip_lo=1e-3),
NormalDomainParam(name='m_pole',
mean=dp_nom['m_pole'],
std=dp_nom['m_pole'] / 10,
clip_lo=1e-3),
NormalDomainParam(name='l_pole',
mean=dp_nom['l_pole'],
std=dp_nom['l_pole'] / 10,
clip_lo=1e-3),
NormalDomainParam(name='d_pole',
mean=dp_nom['d_pole'],
std=dp_nom['d_pole'] / 10,
clip_lo=1e-3),
NormalDomainParam(name='tau_max',
mean=dp_nom['tau_max'],
std=dp_nom['tau_max'] / 10,
clip_lo=1e-3))
def get_default_randomizer_pi() -> DomainRandomizer:
"""
Get the default randomizer for the `PlanarInsertSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.rcspysim.planar_insert import PlanarInsertSim
dp_nom = PlanarInsertSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='link1_mass',
mean=dp_nom['link1_mass'],
std=dp_nom['link1_mass'] / 5,
clip_lo=1e-2),
NormalDomainParam(name='link2_mass',
mean=dp_nom['link2_mass'],
std=dp_nom['link2_mass'] / 5,
clip_lo=1e-2),
NormalDomainParam(name='link3_mass',
mean=dp_nom['link3_mass'],
std=dp_nom['link3_mass'] / 5,
clip_lo=1e-2),
NormalDomainParam(name='link4_mass',
mean=dp_nom['link4_mass'],
std=dp_nom['link4_mass'] / 5,
clip_lo=1e-2),
NormalDomainParam(name='link5_mass',
mean=dp_nom['link4_mass'],
std=dp_nom['link4_mass'] / 5,
clip_lo=1e-2),
UniformDomainParam(name='upperwall_pos_offset_z',
mean=0,
halfspan=0.05,
clip_lo=0) # only increase the gap
)
def create_default_randomizer_pend() -> DomainRandomizer:
"""
Create the default randomizer for the `PendulumSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.pendulum import PendulumSim
dp_nom = PendulumSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name="gravity_const",
mean=dp_nom["gravity_const"],
std=dp_nom["gravity_const"] / 10,
clip_lo=1e-3),
NormalDomainParam(name="pole_mass",
mean=dp_nom["pole_mass"],
std=dp_nom["pole_mass"] / 10,
clip_lo=1e-3),
NormalDomainParam(name="pole_length",
mean=dp_nom["pole_length"],
std=dp_nom["pole_length"] / 10,
clip_lo=1e-3),
NormalDomainParam(name="pole_damping",
mean=dp_nom["pole_damping"],
std=dp_nom["pole_damping"] / 10,
clip_lo=1e-3),
NormalDomainParam(name="torque_thold",
mean=dp_nom["torque_thold"],
std=dp_nom["torque_thold"] / 10,
clip_lo=1e-3),
)
def get_default_randomizer_bl() -> DomainRandomizer:
"""
Get the default randomizer for the `BoxLifting`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.rcspysim.box_shelving import BoxShelvingSim
dp_nom = BoxShelvingSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='box_length',
mean=dp_nom['box_length'],
std=dp_nom['box_length'] / 10),
NormalDomainParam(name='box_width',
mean=dp_nom['box_width'],
std=dp_nom['box_width'] / 10),
NormalDomainParam(name='box_mass',
mean=dp_nom['box_mass'],
std=dp_nom['box_mass'] / 5),
UniformDomainParam(name='box_friction_coefficient',
mean=dp_nom['box_friction_coefficient'],
halfspan=dp_nom['box_friction_coefficient'] / 5,
clip_lo=1e-5),
NormalDomainParam(name='basket_mass',
mean=dp_nom['basket_mass'],
std=dp_nom['basket_mass'] / 5),
UniformDomainParam(name='basket_friction_coefficient',
mean=dp_nom['basket_friction_coefficient'],
halfspan=dp_nom['basket_friction_coefficient'] / 5,
clip_lo=1e-5),
)
def get_empty_randomizer() -> DomainRandomizer:
"""
Get an empty randomizer independent of the environment to be filled later (using `add_domain_params`).
:return: empty randomizer
"""
return DomainRandomizer()
def create_default_randomizer_qbb() -> DomainRandomizer:
"""
Create the default randomizer for the `QBallBalancerSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.quanser_ball_balancer import QBallBalancerSim
dp_nom = QBallBalancerSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='g', mean=dp_nom['g'], std=dp_nom['g']/10, clip_lo=1e-4),
NormalDomainParam(name='m_ball', mean=dp_nom['m_ball'], std=dp_nom['m_ball']/5, clip_lo=1e-4),
NormalDomainParam(name='r_ball', mean=dp_nom['r_ball'], std=dp_nom['r_ball']/5, clip_lo=1e-3),
NormalDomainParam(name='l_plate', mean=dp_nom['l_plate'], std=dp_nom['l_plate']/5, clip_lo=5e-2),
NormalDomainParam(name='r_arm', mean=dp_nom['r_arm'], std=dp_nom['r_arm']/5, clip_lo=1e-4),
NormalDomainParam(name='K_g', mean=dp_nom['K_g'], std=dp_nom['K_g']/4, clip_lo=1e-2),
NormalDomainParam(name='J_l', mean=dp_nom['J_l'], std=dp_nom['J_l']/4, clip_lo=1e-6),
NormalDomainParam(name='J_m', mean=dp_nom['J_m'], std=dp_nom['J_m']/4, clip_lo=1e-9),
NormalDomainParam(name='k_m', mean=dp_nom['k_m'], std=dp_nom['k_m']/4, clip_lo=1e-4),
NormalDomainParam(name='R_m', mean=dp_nom['R_m'], std=dp_nom['R_m']/4, clip_lo=1e-4),
UniformDomainParam(name='eta_g', mean=dp_nom['eta_g'], halfspan=dp_nom['eta_g']/4, clip_lo=1e-4, clip_up=1),
UniformDomainParam(name='eta_m', mean=dp_nom['eta_m'], halfspan=dp_nom['eta_m']/4, clip_lo=1e-4, clip_up=1),
UniformDomainParam(name='B_eq', mean=dp_nom['B_eq'], halfspan=dp_nom['B_eq']/4, clip_lo=1e-4),
UniformDomainParam(name='c_frict', mean=dp_nom['c_frict'], halfspan=dp_nom['c_frict']/4, clip_lo=1e-4),
UniformDomainParam(name='V_thold_x_pos', mean=dp_nom['V_thold_x_pos'], halfspan=dp_nom['V_thold_x_pos']/3),
UniformDomainParam(name='V_thold_x_neg', mean=dp_nom['V_thold_x_neg'], halfspan=abs(dp_nom['V_thold_x_neg'])/3),
UniformDomainParam(name='V_thold_y_pos', mean=dp_nom['V_thold_y_pos'], halfspan=dp_nom['V_thold_y_pos']/3),
UniformDomainParam(name='V_thold_y_neg', mean=dp_nom['V_thold_y_neg'], halfspan=abs(dp_nom['V_thold_y_neg'])/3),
UniformDomainParam(name='offset_th_x', mean=dp_nom['offset_th_x'], halfspan=6./180*np.pi),
UniformDomainParam(name='offset_th_y', mean=dp_nom['offset_th_y'], halfspan=6./180*np.pi)
)
def default_dummy_randomizer():
return DomainRandomizer(
DomainParam(name='mass', mean=1.2),
DomainParam(name='special', mean=0),
DomainParam(name='length', mean=4),
DomainParam(name='time_delay', mean=13)
)
def create_default_randomizer_bs() -> DomainRandomizer:
"""
Create the default randomizer for the `BoxShelvingSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.rcspysim.box_shelving import BoxShelvingSim
dp_nom = BoxShelvingSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name="box_length",
mean=dp_nom["box_length"],
std=dp_nom["box_length"] / 10),
NormalDomainParam(name="box_width",
mean=dp_nom["box_width"],
std=dp_nom["box_width"] / 10),
NormalDomainParam(name="box_mass",
mean=dp_nom["box_mass"],
std=dp_nom["box_mass"] / 5),
UniformDomainParam(
name="box_friction_coefficient",
mean=dp_nom["box_friction_coefficient"],
halfspan=dp_nom["box_friction_coefficient"] / 5,
clip_lo=1e-5,
),
)
def get_uniform_masses_lengths_randomizer_qq(frac_halfspan: float):
"""
Get a uniform randomizer that applies to all masses and lengths of the Quanser Qube according to a fraction of their
nominal parameter values
:param frac_halfspan: fraction of the nominal parameter value
:return: `DomainRandomizer` with uniformly distributed masses and lengths
"""
from pyrado.environments.pysim.quanser_qube import QQubeSim
dp_nom = QQubeSim.get_nominal_domain_param()
return DomainRandomizer(
UniformDomainParam(name='Mp',
mean=dp_nom['Mp'],
halfspan=dp_nom['Mp'] / frac_halfspan,
clip_lo=1e-3),
UniformDomainParam(name='Mr',
mean=dp_nom['Mr'],
halfspan=dp_nom['Mr'] / frac_halfspan,
clip_lo=1e-3),
UniformDomainParam(name='Lr',
mean=dp_nom['Lr'],
halfspan=dp_nom['Lr'] / frac_halfspan,
clip_lo=1e-2),
UniformDomainParam(name='Lp',
mean=dp_nom['Lp'],
halfspan=dp_nom['Lp'] / frac_halfspan,
clip_lo=1e-2),
)
def default_randomizer():
return DomainRandomizer(
NormalDomainParam(name="mass", mean=1.2, std=0.1, clip_lo=10, clip_up=100),
UniformDomainParam(name="special", mean=0, halfspan=42, clip_lo=-7.4, roundint=True),
NormalDomainParam(name="length", mean=4, std=0.6, clip_up=50.1),
UniformDomainParam(name="time_delay", mean=13, halfspan=6, clip_up=17, roundint=True),
MultivariateNormalDomainParam(name="multidim", mean=10 * to.ones((2,)), cov=2 * to.eye(2), clip_up=11),
)
def default_pert():
return DomainRandomizer(
NormalDomainParam(name='mass', mean=1.2, std=0.1, clip_lo=10, clip_up=100),
UniformDomainParam(name='special', mean=0, halfspan=42, clip_lo=-7.4, roundint=True),
NormalDomainParam(name='length', mean=4, std=0.6, clip_up=50.1),
UniformDomainParam(name='time_delay', mean=13, halfspan=6, clip_up=17, roundint=True),
MultivariateNormalDomainParam(name='multidim', mean=10*to.ones((2,)), cov=2*to.eye(2), clip_up=11)
)
def create_example_randomizer_cata() -> DomainRandomizer:
"""
Create the randomizer for the `CatapultSim` used for the 'illustrative example' in F. Muratore et al, 2019, TAMPI.
:return: randomizer based on the nominal domain parameter values
"""
return DomainRandomizer(
BernoulliDomainParam(name='planet', mean=None, val_0=0, val_1=1, prob_1=0.7, roundint=True)
) # 0 = Mars, 1 = Venus
def test_sprl(ex_dir, env: SimEnv, optimize_mean: bool):
pyrado.set_seed(0)
env = ActNormWrapper(env)
env_sprl_params = [
dict(
name="gravity_const",
target_mean=to.tensor([9.81]),
target_cov_chol_flat=to.tensor([1.0]),
init_mean=to.tensor([9.81]),
init_cov_chol_flat=to.tensor([0.05]),
)
]
radnomizer = DomainRandomizer(
*[SelfPacedDomainParam(**p) for p in env_sprl_params])
env = DomainRandWrapperLive(env, randomizer=radnomizer)
policy = FNNPolicy(env.spec, hidden_sizes=[64, 64], hidden_nonlin=to.tanh)
vfcn_hparam = dict(hidden_sizes=[32, 32], hidden_nonlin=to.relu)
vfcn = FNNPolicy(spec=EnvSpec(env.obs_space, ValueFunctionSpace),
**vfcn_hparam)
critic_hparam = dict(
gamma=0.9844534412010116,
lamda=0.9710614403461155,
num_epoch=10,
batch_size=150,
standardize_adv=False,
lr=0.00016985313083236645,
)
critic = GAE(vfcn, **critic_hparam)
subrtn_hparam = dict(
max_iter=1,
eps_clip=0.12648736789309026,
min_steps=10 * env.max_steps,
num_epoch=3,
batch_size=150,
std_init=0.7573286998997557,
lr=6.999956625305722e-04,
max_grad_norm=1.0,
num_workers=1,
)
algo_hparam = dict(
kl_constraints_ub=8000,
performance_lower_bound=500,
std_lower_bound=0.4,
kl_threshold=200,
max_iter=1,
optimize_mean=optimize_mean,
)
algo = SPRL(env, PPO(ex_dir, env, policy, critic, **subrtn_hparam),
**algo_hparam)
algo.train(snapshot_mode="latest")
assert algo.curr_iter == algo.max_iter
def bob_pert():
return DomainRandomizer(
NormalDomainParam(name='g', mean=9.81, std=0.981, clip_lo=1e-3),
NormalDomainParam(name='r_ball', mean=0.1, std=0.01, clip_lo=1e-3),
NormalDomainParam(name='m_ball', mean=0.5, std=0.05, clip_lo=1e-3),
NormalDomainParam(name='m_beam', mean=3.0, std=0.3, clip_lo=1e-3),
NormalDomainParam(name='d_beam', mean=0.1, std=0.01, clip_lo=1e-3),
NormalDomainParam(name='l_beam', mean=2.0, std=0.2, clip_lo=1e-3),
UniformDomainParam(name='c_frict', mean=0.05, halfspan=0.05),
UniformDomainParam(name='ang_offset', mean=0, halfspan=5.*np.pi/180)
)
def create_bob_setup():
# Environments
env_hparams = dict(dt=1 / 100., max_steps=500)
env_real = BallOnBeamSim(**env_hparams)
env_real.domain_param = dict(
# l_beam=1.95,
# ang_offset=-0.03,
g=10.81)
env_sim = BallOnBeamSim(**env_hparams)
randomizer = DomainRandomizer(
# NormalDomainParam(name='l_beam', mean=0, std=1e-12, clip_lo=1.5, clip_up=3.5),
# UniformDomainParam(name='ang_offset', mean=0, halfspan=1e-12),
NormalDomainParam(name='g', mean=0, std=1e-12), )
env_sim = DomainRandWrapperLive(env_sim, randomizer)
dp_map = {
# 0: ('l_beam', 'mean'), 1: ('l_beam', 'std'),
# 2: ('ang_offset', 'mean'), 3: ('ang_offset', 'halfspan')
0: ('g', 'mean'),
1: ('g', 'std')
}
env_sim = MetaDomainRandWrapper(env_sim, dp_map)
# Policies (the behavioral policy needs to be deterministic)
behavior_policy = LinearPolicy(env_sim.spec,
feats=FeatureStack(
[identity_feat, sin_feat]))
behavior_policy.param_values = to.tensor(
[3.8090, -3.8036, -1.0786, -2.4510, -0.9875, -1.3252, 3.1503, 1.4443])
prior = DomainRandomizer(
# NormalDomainParam(name='l_beam', mean=2.05, std=2.05/10),
# UniformDomainParam(name='ang_offset', mean=0.03, halfspan=0.03/10),
NormalDomainParam(name='g', mean=8.81, std=8.81 / 10), )
# trafo_mask = [False, True, False, True]
trafo_mask = [True, True]
ddp_policy = DomainDistrParamPolicy(mapping=dp_map,
trafo_mask=trafo_mask,
prior=prior,
scale_params=True)
return env_sim, env_real, env_hparams, dp_map, behavior_policy, ddp_policy
def get_default_randomizer_wambic() -> DomainRandomizer:
from pyrado.environments.mujoco.wam import WAMBallInCupSim
dp_nom = WAMBallInCupSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='cup_scale',
mean=dp_nom['cup_scale'],
std=dp_nom['cup_scale'] / 5,
clip_lo=0.6), # ball needs to fit into the cup
NormalDomainParam(name='rope_length',
mean=dp_nom['rope_length'],
std=dp_nom['rope_length'] / 10,
clip_lo=0.2), # rope won't be less then 10cm shorter
)
def create_default_randomizer_omo() -> DomainRandomizer:
"""
Create the default randomizer for the `OneMassOscillatorSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.one_mass_oscillator import OneMassOscillatorSim
dp_nom = OneMassOscillatorSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='m', mean=dp_nom['m'], std=dp_nom['m']/3, clip_lo=1e-3),
NormalDomainParam(name='k', mean=dp_nom['k'], std=dp_nom['k']/3, clip_lo=1e-3),
NormalDomainParam(name='d', mean=dp_nom['d'], std=dp_nom['d']/3, clip_lo=1e-3)
)
def create_default_randomizer_cata() -> DomainRandomizer:
"""
Create the default randomizer for the `CatapultSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.one_step.catapult import CatapultSim
dp_nom = CatapultSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='g', mean=dp_nom['g'], std=dp_nom['g']/10, clip_lo=1e-3),
NormalDomainParam(name='k', mean=dp_nom['k'], std=dp_nom['k']/5, clip_lo=1e-3),
NormalDomainParam(name='x', mean=dp_nom['x'], std=dp_nom['x']/5, clip_lo=1e-3)
)
def create_default_randomizer_qq() -> DomainRandomizer:
"""
Create the default randomizer for the `QQubeSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.quanser_qube import QQubeSim
dp_nom = QQubeSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name="gravity_const",
mean=dp_nom["gravity_const"],
std=dp_nom["gravity_const"] / 10,
clip_lo=1e-3),
NormalDomainParam(name="motor_resistance",
mean=dp_nom["motor_resistance"],
std=dp_nom["motor_resistance"] / 5,
clip_lo=1e-3),
NormalDomainParam(name="motor_back_emf",
mean=dp_nom["motor_back_emf"],
std=dp_nom["motor_back_emf"] / 5,
clip_lo=1e-4),
NormalDomainParam(name="mass_rot_pole",
mean=dp_nom["mass_rot_pole"],
std=dp_nom["mass_rot_pole"] / 5,
clip_lo=1e-4),
NormalDomainParam(name="length_rot_pole",
mean=dp_nom["length_rot_pole"],
std=dp_nom["length_rot_pole"] / 5,
clip_lo=1e-4),
NormalDomainParam(name="damping_rot_pole",
mean=dp_nom["damping_rot_pole"],
std=dp_nom["damping_rot_pole"] / 4,
clip_lo=1e-9),
NormalDomainParam(name="mass_pend_pole",
mean=dp_nom["mass_pend_pole"],
std=dp_nom["mass_pend_pole"] / 5,
clip_lo=1e-4),
NormalDomainParam(name="length_pend_pole",
mean=dp_nom["length_pend_pole"],
std=dp_nom["length_pend_pole"] / 5,
clip_lo=1e-4),
NormalDomainParam(
name="damping_pend_pole",
mean=dp_nom["damping_pend_pole"],
std=dp_nom["damping_pend_pole"] / 4,
clip_lo=1e-9,
),
)
def create_default_randomizer_bop() -> DomainRandomizer:
"""
Create the default randomizer for the `BallOnPlateSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.rcspysim.ball_on_plate import BallOnPlateSim
dp_nom = BallOnPlateSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name="ball_mass",
mean=dp_nom["ball_mass"],
std=dp_nom["ball_mass"] / 3,
clip_lo=1e-2),
NormalDomainParam(name="ball_radius",
mean=dp_nom["ball_radius"],
std=dp_nom["ball_radius"] / 3,
clip_lo=1e-2),
NormalDomainParam(name="ball_com_x",
mean=dp_nom["ball_com_x"],
std=0.003),
NormalDomainParam(name="ball_com_y",
mean=dp_nom["ball_com_y"],
std=0.003),
NormalDomainParam(name="ball_com_z",
mean=dp_nom["ball_com_z"],
std=0.003),
UniformDomainParam(
name="ball_friction_coefficient",
mean=dp_nom["ball_friction_coefficient"],
halfspan=dp_nom["ball_friction_coefficient"],
clip_lo=0,
clip_hi=1,
),
UniformDomainParam(
name="ball_rolling_friction_coefficient",
mean=dp_nom["ball_rolling_friction_coefficient"],
halfspan=dp_nom["ball_rolling_friction_coefficient"],
clip_lo=0,
clip_hi=1,
),
# Vortex only
UniformDomainParam(name="ball_slip",
mean=dp_nom["ball_slip"],
halfspan=dp_nom["ball_slip"],
clip_lo=0)
# UniformDomainParam(name='ball_linearvelocitydamnping', mean=0., halfspan=1e-4),
# UniformDomainParam(name='ball_angularvelocitydamnping', mean=0., halfspan=1e-4)
)
def create_default_randomizer_wambic() -> DomainRandomizer:
from pyrado.environments.mujoco.wam import WAMBallInCupSim
dp_nom = WAMBallInCupSim.get_nominal_domain_param()
return DomainRandomizer(
# Ball needs to fit into the cup
NormalDomainParam(name='cup_scale', mean=dp_nom['cup_scale'], std=dp_nom['cup_scale']/5, clip_lo=0.65),
# Rope won't be more than 3cm off
NormalDomainParam(name='rope_length', mean=dp_nom['rope_length'], std=dp_nom['rope_length']/30,
clip_lo=0.27, clip_up=0.33),
NormalDomainParam(name='ball_mass', mean=dp_nom['ball_mass'], std=dp_nom['ball_mass']/10, clip_lo=1e-2),
UniformDomainParam(name='joint_damping', mean=dp_nom['joint_damping'], halfspan=dp_nom['joint_damping']/2,
clip_lo=0.),
UniformDomainParam(name='joint_stiction', mean=dp_nom['joint_stiction'], halfspan=dp_nom['joint_stiction']/2,
clip_lo=0.),
UniformDomainParam(name='rope_damping', mean=dp_nom['rope_damping'], halfspan=dp_nom['rope_damping']/2,
clip_lo=1e-6),
)
def get_default_randomizer_qq() -> DomainRandomizer:
"""
Get the default randomizer for the `QQubeSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.quanser_qube import QQubeSim
dp_nom = QQubeSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='g',
mean=dp_nom['g'],
std=dp_nom['g'] / 5,
clip_lo=1e-3),
NormalDomainParam(name='Rm',
mean=dp_nom['Rm'],
std=dp_nom['Rm'] / 5,
clip_lo=1e-3),
NormalDomainParam(name='km',
mean=dp_nom['km'],
std=dp_nom['km'] / 5,
clip_lo=1e-4),
NormalDomainParam(name='Mr',
mean=dp_nom['Mr'],
std=dp_nom['Mr'] / 5,
clip_lo=1e-4),
NormalDomainParam(name='Lr',
mean=dp_nom['Lr'],
std=dp_nom['Lr'] / 5,
clip_lo=1e-4),
NormalDomainParam(name='Dr',
mean=dp_nom['Dr'],
std=dp_nom['Dr'] / 5,
clip_lo=1e-9),
NormalDomainParam(name='Mp',
mean=dp_nom['Mp'],
std=dp_nom['Mp'] / 5,
clip_lo=1e-4),
NormalDomainParam(name='Lp',
mean=dp_nom['Lp'],
std=dp_nom['Lp'] / 5,
clip_lo=1e-4),
NormalDomainParam(name='Dp',
mean=dp_nom['Dp'],
std=dp_nom['Dp'] / 5,
clip_lo=1e-9))
def create_default_randomizer_bob() -> DomainRandomizer:
"""
Create the default randomizer for the `BallOnBeamSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.ball_on_beam import BallOnBeamSim
dp_nom = BallOnBeamSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name='g', mean=dp_nom['g'], std=dp_nom['g']/10, clip_lo=1e-4),
NormalDomainParam(name='m_ball', mean=dp_nom['m_ball'], std=dp_nom['m_ball']/5, clip_lo=1e-4),
NormalDomainParam(name='r_ball', mean=dp_nom['r_ball'], std=dp_nom['r_ball']/5, clip_lo=1e-4),
NormalDomainParam(name='m_beam', mean=dp_nom['m_beam'], std=dp_nom['m_beam']/5, clip_lo=1e-3),
NormalDomainParam(name='l_beam', mean=dp_nom['l_beam'], std=dp_nom['l_beam']/5, clip_lo=1e-3),
NormalDomainParam(name='d_beam', mean=dp_nom['d_beam'], std=dp_nom['d_beam']/5, clip_lo=1e-3),
UniformDomainParam(name='c_frict', mean=dp_nom['c_frict'], halfspan=dp_nom['c_frict'], clip_lo=0),
UniformDomainParam(name='ang_offset', mean=0./180*np.pi, halfspan=0.1/180*np.pi)
)
def create_default_randomizer_bob() -> DomainRandomizer:
"""
Create the default randomizer for the `BallOnBeamSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.ball_on_beam import BallOnBeamSim
dp_nom = BallOnBeamSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name="gravity_const",
mean=dp_nom["gravity_const"],
std=dp_nom["gravity_const"] / 10,
clip_lo=1e-4),
NormalDomainParam(name="ball_mass",
mean=dp_nom["ball_mass"],
std=dp_nom["ball_mass"] / 5,
clip_lo=1e-4),
NormalDomainParam(name="ball_radius",
mean=dp_nom["ball_radius"],
std=dp_nom["ball_radius"] / 5,
clip_lo=1e-4),
NormalDomainParam(name="beam_mass",
mean=dp_nom["beam_mass"],
std=dp_nom["beam_mass"] / 5,
clip_lo=1e-3),
NormalDomainParam(name="beam_length",
mean=dp_nom["beam_length"],
std=dp_nom["beam_length"] / 5,
clip_lo=1e-3),
NormalDomainParam(name="beam_thickness",
mean=dp_nom["beam_thickness"],
std=dp_nom["beam_thickness"] / 5,
clip_lo=1e-3),
UniformDomainParam(name="friction_coeff",
mean=dp_nom["friction_coeff"],
halfspan=dp_nom["friction_coeff"],
clip_lo=0),
UniformDomainParam(name="ang_offset",
mean=0.0 / 180 * np.pi,
halfspan=0.1 / 180 * np.pi),
)
def create_default_randomizer_cata() -> DomainRandomizer:
"""
Create the default randomizer for the `CatapultSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.one_step.catapult import CatapultSim
dp_nom = CatapultSim.get_nominal_domain_param()
return DomainRandomizer(
NormalDomainParam(name="gravity_const",
mean=dp_nom["gravity_const"],
std=dp_nom["gravity_const"] / 10,
clip_lo=1e-3),
NormalDomainParam(name="stiffness",
mean=dp_nom["stiffness"],
std=dp_nom["stiffness"] / 5,
clip_lo=1e-3),
NormalDomainParam(name="elongation",
mean=dp_nom["elongation"],
std=dp_nom["elongation"] / 5,
clip_lo=1e-3),
)
def create_default_randomizer_qcp() -> DomainRandomizer:
"""
Create the default randomizer for the `QCartPoleSim`.
:return: randomizer based on the nominal domain parameter values
"""
from pyrado.environments.pysim.quanser_cartpole import QCartPoleSim
dp_nom = QCartPoleSim.get_nominal_domain_param(long=False)
return DomainRandomizer(
NormalDomainParam(name='g', mean=dp_nom['g'], std=dp_nom['g']/10, clip_lo=1e-4),
NormalDomainParam(name='m_cart', mean=dp_nom['m_cart'], std=dp_nom['m_cart']/5, clip_lo=1e-4),
NormalDomainParam(name='m_pole', mean=dp_nom['m_pole'], std=dp_nom['m_pole']/5, clip_lo=1e-4),
NormalDomainParam(name='l_rail', mean=dp_nom['l_rail'], std=dp_nom['l_rail']/5, clip_lo=1e-2),
NormalDomainParam(name='l_pole', mean=dp_nom['l_pole'], std=dp_nom['l_pole']/5, clip_lo=1e-2),
UniformDomainParam(name='eta_m', mean=dp_nom['eta_m'], halfspan=dp_nom['eta_m']/4, clip_lo=1e-4, clip_up=1),
UniformDomainParam(name='eta_g', mean=dp_nom['eta_g'], halfspan=dp_nom['eta_g']/4, clip_lo=1e-4, clip_up=1),
NormalDomainParam(name='K_g', mean=dp_nom['K_g'], std=dp_nom['K_g']/4, clip_lo=1e-4),
NormalDomainParam(name='J_m', mean=dp_nom['J_m'], std=dp_nom['J_m']/4, clip_lo=1e-9),
NormalDomainParam(name='r_mp', mean=dp_nom['r_mp'], std=dp_nom['r_mp']/5, clip_lo=1e-4),
NormalDomainParam(name='R_m', mean=dp_nom['R_m'], std=dp_nom['R_m']/4, clip_lo=1e-4),
NormalDomainParam(name='k_m', mean=dp_nom['k_m'], std=dp_nom['k_m']/4, clip_lo=1e-4),
UniformDomainParam(name='B_eq', mean=dp_nom['B_eq'], halfspan=dp_nom['B_eq']/4, clip_lo=1e-4),
UniformDomainParam(name='B_pole', mean=dp_nom['B_pole'], halfspan=dp_nom['B_pole']/4, clip_lo=1e-4)
)
num_workers=8,
lr_scheduler=lr_scheduler.ExponentialLR,
lr_scheduler_hparam=dict(gamma=0.999),
)
env_sprl_params = [
dict(
name="gravity_const",
target_mean=to.tensor([9.81]),
target_cov_chol_flat=to.tensor([1.0]),
init_mean=to.tensor([9.81]),
init_cov_chol_flat=to.tensor([0.05]),
)
]
env = DomainRandWrapperLive(
env,
randomizer=DomainRandomizer(
*[SelfPacedDomainParam(**p) for p in env_sprl_params]))
sprl_hparam = dict(
kl_constraints_ub=8000,
performance_lower_bound=500,
std_lower_bound=0.4,
kl_threshold=200,
max_iter=args.sprl_iterations,
optimize_mean=not args.cov_only,
)
algo = SPRL(env, PPO(ex_dir, env, policy, critic, **algo_hparam),
**sprl_hparam)
# Save the hyper-parameters
save_dicts_to_yaml(
dict(env=env_hparams, seed=args.seed),
task_args=dict(
final_factor=500,
success_bonus=250,
Q=np.diag([0.5, 1e-4, 4e1]),
R=np.diag([0, 0, 1e-1, 2e-1]),
Q_dev=np.diag([0.0, 0.0, 5]),
# R_dev=np.diag([0., 0., 1e-3, 1e-3])
),
)
env = WAMBallInCupSim(**env_hparams)
# Randomizer
randomizer = DomainRandomizer(
UniformDomainParam(name="cup_scale", mean=1.0, halfspan=0.2),
NormalDomainParam(name="rope_length", mean=0.3, std=0.005),
NormalDomainParam(name="ball_mass", mean=0.021, std=0.001),
UniformDomainParam(name="joint_2_damping", mean=0.05, halfspan=0.05),
UniformDomainParam(name="joint_2_dryfriction", mean=0.1, halfspan=0.1),
)
env = DomainRandWrapperLive(env, randomizer)
# Policy
bounds = ([0.0, 0.25, 0.5], [1.0, 1.5, 2.5])
policy_hparam = dict(rbf_hparam=dict(num_feat_per_dim=9, bounds=bounds, scale=None), dim_mask=2)
policy = DualRBFLinearPolicy(env.spec, **policy_hparam)
# Algorithm
algo_hparam = dict(
max_iter=15,
pop_size=100,
num_is_samples=10,
pyrado.set_seed(args.seed, verbose=True)
# Environments
env_hparams = dict(dt=1 / 100.0, max_steps=600)
env_real = QQubeSwingUpReal(**env_hparams)
env_sim = QQubeSwingUpSim(**env_hparams)
randomizer = DomainRandomizer(
NormalDomainParam(name="mass_rot_pole",
mean=0.0,
std=1e6,
clip_lo=1e-3),
NormalDomainParam(name="mass_pend_pole",
mean=0.0,
std=1e6,
clip_lo=1e-3),
NormalDomainParam(name="length_rot_pole",
mean=0.0,
std=1e6,
clip_lo=1e-3),
NormalDomainParam(name="length_pend_pole",
mean=0.0,
std=1e6,
clip_lo=1e-3),
)
env_sim = DomainRandWrapperLive(env_sim, randomizer)
dp_map = {
0: ("mass_rot_pole", "mean"),
1: ("mass_rot_pole", "std"),
2: ("mass_pend_pole", "mean"),
3: ("mass_pend_pole", "std"),
4: ("length_rot_pole", "mean"),
