def _create_spaces(self):
# State space (normalized time, since we do not have a simulation)
self._state_space = BoxSpace(np.array([0.]), np.array([1.]))
# Action space (PD controller on 3 joint positions and velocities)
max_act = np.array([
np.pi,
np.pi,
np.pi, # [rad, rad, rad, ...
10 * np.pi,
10 * np.pi,
10 * np.pi
]) # ... rad/s, rad/s, rad/s]
self._act_space = BoxSpace(-max_act,
max_act,
labels=[
r'$q_{1,des}$', r'$q_{3,des}$',
r'$q_{5,des}$', r'$\dot{q}_{1,des}$',
r'$\dot{q}_{3,des}$',
r'$\dot{q}_{5,des}$'
])
# Observation space (normalized time)
self._obs_space = BoxSpace(np.array([0.]),
np.array([1.]),
labels=['$t$'])
def _create_spaces(self):
# State space (normalized time, since we do not have a simulation)
self._state_space = BoxSpace(np.array([0.]), np.array([1.]))
# Action space (PD controller on joint positions and velocities)
if self.num_dof == 4:
self._act_space = act_space_wam_4dof
elif self.num_dof == 7:
self._act_space = act_space_wam_7dof
# Observation space (normalized time)
self._obs_space = BoxSpace(np.array([0.]),
np.array([1.]),
labels=['t'])
def _create_spaces(self):
# State space (joint positions and velocities)
state_shape = (2 * self.num_dof, )
state_up, state_lo = np.full(state_shape, pyrado.inf), np.full(
state_shape, -pyrado.inf)
self._state_space = BoxSpace(state_lo, state_up)
# Action space (PD controller on joint positions and velocities)
if self.num_dof == 4:
self._act_space = act_space_wam_4dof
elif self.num_dof == 7:
self._act_space = act_space_wam_7dof
# Observation space (normalized time)
self._obs_space = BoxSpace(np.array([0.]),
np.array([1.]),
labels=['t'])
def obs_space(self) -> Space:
lo = np.concatenate([
WAM_Q_LIMITS_LO_7DOF[:self._num_dof],
WAM_QD_LIMITS_LO_7DOF[:self._num_dof]
])
up = np.concatenate([
WAM_Q_LIMITS_UP_7DOF[:self._num_dof],
WAM_QD_LIMITS_UP_7DOF[:self._num_dof]
])
return BoxSpace(bound_lo=lo, bound_up=up)
def obs_space(self) -> Space:
# Observation space (normalized time and optionally cup and ball position)
obs_lo, obs_up, labels = [0.0], [1.0], ["t"]
if self.observe_ball:
obs_lo.extend([-3.0, -3.0])
obs_up.extend([3.0, 3.0])
labels.extend(["ball_x", "ball_z"])
if self.observe_cup:
obs_lo.extend([-3.0, -3.0])
obs_up.extend([3.0, 3.0])
labels.extend(["cup_x", "cup_z"])
return BoxSpace(obs_lo, obs_up, labels=labels)
def test_bayrn_power(ex_dir, env: SimEnv, bayrn_hparam: dict):
pyrado.set_seed(0)
# Environments and domain randomization
env_real = deepcopy(env)
env_sim = DomainRandWrapperLive(env, create_zero_var_randomizer(env))
dp_map = create_default_domain_param_map_qq()
env_sim = MetaDomainRandWrapper(env_sim, dp_map)
env_real.domain_param = dict(mass_pend_pole=0.024 * 1.1,
mass_rot_pole=0.095 * 1.1)
env_real = wrap_like_other_env(env_real, env_sim)
# Policy and subroutine
policy_hparam = dict(energy_gain=0.587, ref_energy=0.827)
policy = QQubeSwingUpAndBalanceCtrl(env_sim.spec, **policy_hparam)
subrtn_hparam = dict(
max_iter=1,
pop_size=8,
num_init_states_per_domain=1,
num_is_samples=4,
expl_std_init=0.1,
num_workers=1,
)
subrtn = PoWER(ex_dir, env_sim, policy, **subrtn_hparam)
# Set the boundaries for the GP
dp_nom = inner_env(env_sim).get_nominal_domain_param()
ddp_space = BoxSpace(
bound_lo=np.array([
0.8 * dp_nom["mass_pend_pole"], 1e-8,
0.8 * dp_nom["mass_rot_pole"], 1e-8
]),
bound_up=np.array([
1.2 * dp_nom["mass_pend_pole"], 1e-7,
1.2 * dp_nom["mass_rot_pole"], 1e-7
]),
)
# Create algorithm and train
algo = BayRn(ex_dir,
env_sim,
env_real,
subrtn,
ddp_space,
**bayrn_hparam,
num_workers=1)
algo.train()
assert algo.curr_iter == algo.max_iter or algo.stopping_criterion_met()
def state_space(self) -> Space:
lo = np.concatenate([
WAM_Q_LIMITS_LO_7DOF[:self._num_dof],
WAM_QD_LIMITS_LO_7DOF[:self._num_dof]
])
up = np.concatenate([
WAM_Q_LIMITS_UP_7DOF[:self._num_dof],
WAM_QD_LIMITS_UP_7DOF[:self._num_dof]
])
return BoxSpace(
bound_lo=lo,
bound_up=up,
labels=[f"q_{i}"
for i in range(1, 8)] + [f"qd_{i}" for i in range(1, 8)],
)
def __init__(
self,
mapping: Dict[int, Tuple[str, str]],
trafo_mask: Union[list, to.Tensor],
prior: DomainRandomizer = None,
scale_params: bool = False,
use_cuda: bool = False,
):
"""
Constructor
:param mapping: mapping from subsequent integers to domain distribution parameters, where the first string of
the value tuple is the name of the domain parameter (e.g. mass, length) and the second string is
the name of the distribution parameter (e.g. mean, std). The integers are indices of the numpy
array which come from the algorithm.
:param trafo_mask: every domain parameter that is set to `True` in this mask will be learned via a 'virtual'
parameter, i.e. in sqrt-space, and then finally squared to retrieve the domain parameter.
This transformation is useful to avoid setting a negative variance.
:param prior: prior believe about the distribution parameters in from of a `DomainRandomizer`
:param scale_params: if `True`, the sqrt-transformed policy parameters are scaled in the range of $[-0.5, 0.5]$.
The advantage of this is to make the parameter-based exploration easier.
:param use_cuda: `True` to move the policy to the GPU, `False` (default) to use the CPU
"""
if not isinstance(mapping, dict):
raise pyrado.TypeErr(given=mapping, expected_type=dict)
if not len(trafo_mask) == len(mapping):
raise pyrado.ShapeErr(given=trafo_mask, expected_match=mapping)
self._mapping = mapping
self.mask = to.tensor(trafo_mask, dtype=to.bool)
self.prior = prior
self._scale_params = scale_params
# Define the parameter space by using the Policy.env_spec.act_space
param_spec = EnvSpec(obs_space=EmptySpace(),
act_space=BoxSpace(-pyrado.inf,
pyrado.inf,
shape=len(mapping)))
# Call Policy's constructor
super().__init__(param_spec, use_cuda)
self.params = nn.Parameter(to.empty(param_spec.act_space.flat_dim),
requires_grad=True)
if self._scale_params:
self.param_scaler = None # initialized during init_param()
self.init_param(prior=prior)
def state_space(self) -> Space:
# State space (joint positions and velocities)
state_lo = np.concatenate([
WAM_Q_LIMITS_LO_7DOF[:self._num_dof],
WAM_QD_LIMITS_LO_7DOF[:self._num_dof]
])
state_up = np.concatenate([
WAM_Q_LIMITS_UP_7DOF[:self._num_dof],
WAM_QD_LIMITS_UP_7DOF[:self._num_dof]
])
# Ball and cup (x,y,z)-space
if self.observe_ball:
state_lo = np.r_[state_lo, np.full((3, ), -3.0)]
state_up = np.r_[state_up, np.full((3, ), 3.0)]
if self.observe_cup:
state_lo = np.r_[state_lo, np.full((3, ), -3.0)]
state_up = np.r_[state_up, np.full((3, ), 3.0)]
return BoxSpace(state_lo, state_up)
WAM_Q_LIMITS_LO_7DOF = np.array([-2.6, -2.0, -2.8, -0.9, -4.76, -1.6, -3.0
]) + 5 * np.pi / 180
WAM_Q_LIMITS_UP_7DOF = np.array([+2.6, +2.0, +2.8, +3.1, +1.24, +1.6, +3.0
]) - 5 * np.pi / 180
# Arbitrarily set velocity limits [rad/s]
WAM_QD_LIMITS_LO_7DOF = -np.array([
4 * np.pi, 4 * np.pi, 4 * np.pi, 4 * np.pi, 4 * np.pi, 4 * np.pi, 4 * np.pi
])
WAM_QD_LIMITS_UP_7DOF = +np.array([
4 * np.pi, 4 * np.pi, 4 * np.pi, 4 * np.pi, 4 * np.pi, 4 * np.pi, 4 * np.pi
])
# Torque limits
# 7 DoF
MAX_TORQUE_7DOF = np.array([150.0, 125.0, 40.0, 60.0, 5.0, 5.0, 2.0])
TORQUE_SPACE_WAM_7DOF = BoxSpace(-MAX_TORQUE_7DOF, MAX_TORQUE_7DOF)
# 4 DoF
MAX_TORQUE_4DOF = MAX_TORQUE_7DOF[:4]
TORQUE_SPACE_WAM_4DOF = BoxSpace(-MAX_TORQUE_4DOF, MAX_TORQUE_4DOF)
# Default PD gains from robcom / SL
WAM_PGAINS_7DOF = np.array([200.0, 300.0, 100.0, 100.0, 10.0, 10.0, 2.5])
WAM_DGAINS_7DOF = np.array([7.0, 15.0, 5.0, 2.5, 0.3, 0.3, 0.05])
WAM_PGAINS_4DOF = WAM_PGAINS_7DOF[:4]
WAM_DGAINS_4DOF = WAM_DGAINS_7DOF[:4]
# Desired initial joint angles
# 7 DoF
INIT_QPOS_DES_7DOF = np.array([0.0, 0.5876, 0.0, 1.36, 0.0, -0.321, -1.57])
# 4 DoF arm
INIT_QPOS_DES_4DOF = np.array([0.0, 0.6, 0.0, 1.25])
raise pyrado.ValueErr(
given=args.env_name,
eq_constraint=
f"{QQubeSwingUpReal.name}, {QCartPoleSwingUpReal.name} or {WAMReal.name}"
)
print_cbt(f"Set the {env_real.name} environment.", "g")
# Create the policy
if args.mode.lower() == "chirp":
def fcn_of_time(t: float):
act = max_amp * chirp(t, f0=3, f1=0, t1=t_end, method="linear")
return act.repeat(env_real.act_space.flat_dim)
elif args.mode.lower() == "skyline":
t_intvl_space = BoxSpace(dt, 100 * dt, shape=(1, ))
val_space = BoxSpace(-abs(max_amp), abs(max_amp), shape=(1, ))
act_predef = skyline(dt, t_end, t_intvl_space, val_space)[1]
def fcn_of_time(t: float):
act = act_predef[int(t / dt)]
return act.repeat(env_real.act_space.flat_dim)
elif args.mode.lower() == "sin":
def fcn_of_time(t: float):
act = max_amp * np.sin(2 * np.pi * t * 2.0) # 2 Hz
return act.repeat(env_real.act_space.flat_dim)
elif args.mode.lower() == "wam_sin":
fcn_of_time = wam_jsp_7dof_sin
from pyrado.policies.feed_forward.linear import LinearPolicy
from pyrado.policies.features import *
from pyrado.policies.recurrent.two_headed_rnn import TwoHeadedRNNPolicyBase
from pyrado.sampling.rollout import rollout
from pyrado.sampling.step_sequence import StepSequence
from pyrado.utils.data_sets import TimeSeriesDataSet
from pyrado.utils.data_types import RenderMode
from pyrado.utils.functions import skyline
from pyrado.utils.nn_layers import IndiNonlinLayer
from tests.conftest import m_needs_bullet, m_needs_mujoco, m_needs_rcs, m_needs_libtorch
from tests.environment_wrappers.mock_env import MockEnv
@pytest.fixture(scope='function',
params=[
(skyline(0.02, 20., BoxSpace(0.5, 3, shape=(1,)), BoxSpace(-2., 3., shape=(1,)))[1],
0.7, 50, False, True),
],
ids=['skyline_0.8_50_notstdized_scaled'])
def tsdataset(request):
return TimeSeriesDataSet(
data=to.from_numpy(request.param[0]),
ratio_train=request.param[1],
window_size=request.param[2],
standardize_data=request.param[3],
scale_min_max_data=request.param[4]
)
@pytest.mark.features
@pytest.mark.parametrize(
# max_iter=300,
# min_steps=23*env_sim.max_steps,
# num_epoch=7,
# eps_clip=0.0744,
# batch_size=500,
# std_init=0.9074,
# lr=3.446e-04,
# max_grad_norm=1.,
# num_workers=12,
# )
# subrtn = PPO(ex_dir, env_sim, policy, critic, **subrtn_hparam)
# Set the boundaries for the GP
dp_nom = QQubeSwingUpSim.get_nominal_domain_param()
ddp_space = BoxSpace(
bound_lo=np.array([0.8*dp_nom['Mp'], 1e-12, 0.8*dp_nom['Mr'], 1e-12]),
bound_up=np.array([1.2*dp_nom['Mp'], 1e-11, 1.2*dp_nom['Mr'], 1e-11])
)
# Algorithm
bayrn_hparam = dict(
max_iter=15,
acq_fc='UCB',
acq_param=dict(beta=0.25),
acq_restarts=500,
acq_samples=1000,
num_init_cand=4,
warmstart=False,
num_eval_rollouts_real=10, # sim-2-sim
# thold_succ_subrtn=300,
)
import numpy as np
import torch as to
from matplotlib import pyplot as plt
from matplotlib import ticker
import pyrado
from pyrado.policies.features import FeatureStack, RBFFeat
from pyrado.policies.linear import LinearPolicy
from pyrado.spaces import BoxSpace
from pyrado.utils.data_types import EnvSpec
if __name__ == '__main__':
# Define some arbitrary EnvSpec
obs_space = BoxSpace(bound_lo=np.array([-5., -12.]), bound_up=np.array([10., 6.]))
act_space = BoxSpace(bound_lo=np.array([-1.]), bound_up=np.array([1.]))
spec = EnvSpec(obs_space, act_space)
num_fpd = 5
num_eval_points = 500
policy_hparam = dict(
feats=FeatureStack([RBFFeat(num_feat_per_dim=num_fpd, bounds=obs_space.bounds, scale=None)])
)
policy = LinearPolicy(spec, **policy_hparam)
eval_grid_0 = to.linspace(-5., 10, num_eval_points)
eval_grid_1 = to.linspace(-12., 6, num_eval_points)
eval_grid = to.stack([eval_grid_0, eval_grid_1], dim=1)
feat_vals = to.empty(num_eval_points, num_fpd*obs_space.flat_dim)
plt.plot(x_grid, noisy_nonlin_fcn(x=x_grid, f=f, noise_std=noise_std), alpha=0.2)
plt.scatter(x.data.numpy(), obj_fcn().numpy(), s=16, color=colors[e])
plt.xlabel("$x$")
plt.ylabel("$f(x)$")
plt.legend()
plt.show()
assert noisy_nonlin_fcn(x, f=f, noise_std=noise_std) < noisy_nonlin_fcn(x_init, f=f, noise_std=noise_std)
@pytest.mark.parametrize("dt", [0.1], ids=["0.1"])
@pytest.mark.parametrize("t_end", [6.0], ids=["1."])
@pytest.mark.parametrize(
"t_intvl_space",
[
BoxSpace(0.1, 0.11, shape=1),
BoxSpace(0.123, 0.456, shape=1),
BoxSpace(10.0, 20.0, shape=1),
],
ids=["small_time_intvl", "real_time_intvl", "large_time_intvl"],
)
@pytest.mark.parametrize("val_space", [BoxSpace(-5.0, 3.0, shape=1)], ids=["-5_to_3"])
def test_skyline(dt: Union[int, float], t_end: Union[int, float], t_intvl_space: BoxSpace, val_space: BoxSpace):
# Create the skyline function
t, vals = skyline(dt, t_end, t_intvl_space, val_space)
assert isinstance(t, np.ndarray) and isinstance(vals, np.ndarray)
assert len(t) == len(vals)
def test_check_prompt():
with completion_context("Works fine", color="g"):
# num_init_states_per_domain=6,
# num_is_samples=10,
# expl_std_init=2.0,
# expl_std_min=0.02,
# symm_sampling=False,
# num_workers=32,
# )
# subrtn = PoWER(ex_dir, env_sim, policy, **subrtn_hparam)
# Set the boundaries for the GP
dp_nom = QQubeSwingUpSim.get_nominal_domain_param()
ddp_space = BoxSpace(
bound_lo=np.array([
0.8 * dp_nom["mass_pend_pole"], 1e-8,
0.8 * dp_nom["mass_rot_pole"], 1e-8
]),
bound_up=np.array([
1.2 * dp_nom["mass_pend_pole"], 1e-7,
1.2 * dp_nom["mass_rot_pole"], 1e-7
]),
)
# Algorithm
bayrn_hparam = dict(
max_iter=15,
acq_fc="UCB",
acq_param=dict(beta=0.25),
acq_restarts=500,
acq_samples=1000,
num_init_cand=4,
warmstart=False,
num_eval_rollouts_real=100,
alpha=0.2)
plt.scatter(x.data.numpy(), obj_fcn().numpy(), s=16, color=colors[e])
plt.xlabel('$x$')
plt.ylabel('$f(x)$')
plt.legend()
plt.show()
assert noisy_nonlin_fcn(x, f=f, noise_std=noise_std) < noisy_nonlin_fcn(
x_init, f=f, noise_std=noise_std)
@pytest.mark.parametrize('dt', [0.1], ids=['0.1'])
@pytest.mark.parametrize('t_end', [6.], ids=['1.'])
@pytest.mark.parametrize(
't_intvl_space', [
BoxSpace(0.1, 0.11, shape=1),
BoxSpace(0.123, 0.456, shape=1),
BoxSpace(10., 20., shape=1),
],
ids=['small_time_intvl', 'real_time_intvl', 'large_time_intvl'])
@pytest.mark.parametrize('val_space', [BoxSpace(-5., 3., shape=1)],
ids=['-5_to_3'])
def test_skyline(dt: Union[int, float], t_end: Union[int, float],
t_intvl_space: BoxSpace, val_space: BoxSpace):
# Create the skyline function
t, vals = skyline(dt, t_end, t_intvl_space, val_space)
assert isinstance(t, np.ndarray) and isinstance(vals, np.ndarray)
assert len(t) == len(vals)
def test_check_prompt():
if env_name == QBallBalancerSim.name:
env = QBallBalancerSim(dt=dt)
elif env_name == QCartPoleSwingUpSim.name:
env = QCartPoleSwingUpSim(dt=dt)
elif env_name == QQubeSwingUpSim.name:
env = QQubeSwingUpSim(dt=dt)
elif env_name == "wam-bic": # avoid loading mujoco
from pyrado.environments.mujoco.wam_bic import WAMBallInCupSim
env = WAMBallInCupSim(num_dof=4)
env.init_space = BoxSpace(-pyrado.inf,
pyrado.inf,
shape=env.init_space.shape)
elif env_name == "wam-jsc": # avoid loading mujoco
from pyrado.environments.mujoco.wam_jsc import WAMJointSpaceCtrlSim
env = WAMJointSpaceCtrlSim(num_dof=7)
env.init_space = BoxSpace(-pyrado.inf,
pyrado.inf,
shape=env.init_space.shape)
elif env_name == "mg-ik": # avoid loading _rcsenv
from pyrado.environments.rcspysim.mini_golf import MiniGolfIKSim
env = MiniGolfIKSim(dt=dt)
std_init=0.7573286998997557,
lr=6.999956625305722e-04,
max_grad_norm=1.,
num_workers=8,
lr_scheduler=lr_scheduler.ExponentialLR,
lr_scheduler_hparam=dict(gamma=0.999))
subrtn = PPO(ex_dir, env_sim, policy, critic, **subrtn_hparam)
# Set the boundaries for the GP
dp_nom = QQubeSwingUpSim.get_nominal_domain_param()
ddp_space = BoxSpace(bound_lo=np.array([
0.8 * dp_nom['Mp'], dp_nom['Mp'] / 5000, 0.8 * dp_nom['Mr'],
dp_nom['Mr'] / 5000, 0.8 * dp_nom['Lp'], dp_nom['Lp'] / 5000,
0.8 * dp_nom['Lr'], dp_nom['Lr'] / 5000
]),
bound_up=np.array([
1.2 * dp_nom['Mp'], dp_nom['Mp'] / 20,
1.2 * dp_nom['Mr'], dp_nom['Mr'] / 20,
1.2 * dp_nom['Lp'], dp_nom['Lp'] / 20,
1.2 * dp_nom['Lr'], dp_nom['Lr'] / 20
]))
# policy_init = to.load(osp.join(pyrado.EXP_DIR, QQubeSwingUpSim.name, PPO.name, 'EXP_NAME', 'policy.pt'))
# valuefcn_init = to.load(osp.join(pyrado.EXP_DIR, QQubeSwingUpSim.name, PPO.name, 'EXP_NAME', 'valuefcn.pt'))
# Algorithm
bayrn_hparam = dict(
thold_succ=300.,
max_iter=15,
acq_fc='EI',
# acq_param=dict(beta=0.2),
dp_nom = WAMBallInCupSim.get_nominal_domain_param()
ddp_space = BoxSpace(
bound_lo=np.array(
[
0.95 * dp_nom["rope_length"],
dp_nom["rope_length"] / 1000,
0.0 * dp_nom["rope_damping"],
dp_nom["rope_damping"] / 100,
0.85 * dp_nom["ball_mass"],
dp_nom["ball_mass"] / 1000,
0.0 * dp_nom["joint_2_dryfriction"],
dp_nom["joint_2_dryfriction"] / 100,
0.0 * dp_nom["joint_2_damping"],
dp_nom["joint_2_damping"] / 100,
]
),
bound_up=np.array(
[
1.05 * dp_nom["rope_length"],
dp_nom["rope_length"] / 20,
2 * dp_nom["rope_damping"],
dp_nom["rope_damping"] / 2,
1.15 * dp_nom["ball_mass"],
dp_nom["ball_mass"] / 10,
2 * dp_nom["joint_2_dryfriction"],
dp_nom["joint_2_dryfriction"] / 2,
2 * dp_nom["joint_2_damping"],
dp_nom["joint_2_damping"] / 2,
]
),
)
)
subrtn = PPO(ex_dir, env_sim, policy, critic, **subrtn_hparam)
# Set the boundaries for the GP
dp_nom = QQubeSwingUpSim.get_nominal_domain_param()
ddp_space = BoxSpace(
bound_lo=np.array([
0.8 * dp_nom["mass_pend_pole"],
dp_nom["mass_pend_pole"] / 5000,
0.8 * dp_nom["mass_rot_pole"],
dp_nom["mass_rot_pole"] / 5000,
0.8 * dp_nom["length_pend_pole"],
dp_nom["length_pend_pole"] / 5000,
0.8 * dp_nom["length_rot_pole"],
dp_nom["length_rot_pole"] / 5000,
]),
bound_up=np.array([
1.2 * dp_nom["mass_pend_pole"],
dp_nom["mass_pend_pole"] / 20,
1.2 * dp_nom["mass_rot_pole"],
dp_nom["mass_rot_pole"] / 20,
1.2 * dp_nom["length_pend_pole"],
dp_nom["length_pend_pole"] / 20,
1.2 * dp_nom["length_rot_pole"],
dp_nom["length_rot_pole"] / 20,
]),
)
# policy_init = to.load(osp.join(pyrado.EXP_DIR, QQubeSwingUpSim.name, PPO.name, 'EXP_NAME', 'policy.pt'))
# valuefcn_init = to.load(osp.join(pyrado.EXP_DIR, QQubeSwingUpSim.name, PPO.name, 'EXP_NAME', 'valuefcn.pt'))
# Algorithm
def state_space(self):
max_state = np.array([100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0])
return BoxSpace(-max_state, max_state, labels=["x_1", "x_2", "x_2", "x_3", "x_5", "x_6", "x_7", "x_8"])
def state_space(self) -> Space:
# Normalized time
return BoxSpace(np.array([0.0]), np.array([1.0]), labels=["t"])
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),
)
# --------------
# Other Fixtures
# --------------
@pytest.fixture(
scope="function",
params=[
(skyline(0.02, 20.0, BoxSpace(0.5, 3, shape=(1,)), BoxSpace(-2.0, 3.0, shape=(1,)))[1], 0.7, 50, False, True),
],
ids=["skyline_0.8_50_notstdized_scaled"],
)
def dataset_ts(request):
return TimeSeriesDataSet(
data=to.from_numpy(request.param[0]).to(dtype=to.get_default_dtype()),
ratio_train=request.param[1],
window_size=request.param[2],
standardize_data=request.param[3],
scale_min_max_data=request.param[4],
)
# Select the data set
data_set_name = args.mode or "skyline"
# Experiment
ex_dir = setup_experiment(TSPred.name, ADNPolicy.name)
# Set seed if desired
pyrado.set_seed(args.seed, verbose=True)
# Load the data
if data_set_name == "skyline":
dt = 0.01
_, vals = skyline(dt=dt,
t_end=20.0,
t_intvl_space=BoxSpace(0.5, 3, shape=(1, )),
val_space=BoxSpace(-2.0, 3.0, shape=(1, )))
data = to.from_numpy(vals).to(dtype=to.get_default_dtype()).view(-1, 1)
else:
data = pd.read_csv(
osp.join(pyrado.PERMA_DIR, "misc", f"{data_set_name}.csv"))
if data_set_name == "daily_min_temperatures":
data = to.tensor(data["Temp"].values,
dtype=to.get_default_dtype()).view(-1, 1)
dt = 1.0
elif data_set_name == "monthly_sunspots":
data = to.tensor(data["Sunspots"].values,
dtype=to.get_default_dtype()).view(-1, 1)
dt = 1.0
elif "oscillation" in data_set_name:
data = to.tensor(data["Positions"].values,
5: ('ball_mass', 'std'),
6: ('joint_stiction', 'mean'),
7: ('joint_stiction', 'halfspan'),
8: ('joint_damping', 'mean'),
9: ('joint_damping', 'halfspan'),
}
env_sim = MetaDomainRandWrapper(env_sim, dp_map)
# Set the boundaries for the GP (must be consistent with dp_map)
dp_nom = WAMBallInCupSim.get_nominal_domain_param()
ddp_space = BoxSpace(
bound_lo=np.array([0.95*dp_nom['rope_length'], dp_nom['rope_length']/1000,
0.*dp_nom['rope_damping'], dp_nom['rope_damping']/100,
0.85*dp_nom['ball_mass'], dp_nom['ball_mass']/1000,
0.*dp_nom['joint_stiction'], dp_nom['joint_stiction']/100,
0.*dp_nom['joint_damping'], dp_nom['joint_damping']/100]),
bound_up=np.array([1.05*dp_nom['rope_length'], dp_nom['rope_length']/20,
2*dp_nom['rope_damping'], dp_nom['rope_damping']/2,
1.15*dp_nom['ball_mass'], dp_nom['ball_mass']/10,
2*dp_nom['joint_stiction'], dp_nom['joint_stiction']/2,
2*dp_nom['joint_damping'], dp_nom['joint_damping']/2])
)
# Setting the ip address to None ensures that robcom does not try to connect to the server pc
env_real_hparams = dict(
num_dof=4,
max_steps=1750,
)
env_real = WAMBallInCupRealEpisodic(**env_real_hparams)
# Policy
policy_hparam = dict(
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER
# IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import numpy as np
from pyrado.spaces import BoxSpace
# 4 DoF arm, 2 DoF actuated
init_qpos_des_4dof = np.array([0., 0.6, 0., 1.25])
act_min_wam_4dof = np.array([-1.985, -0.9, -10 * np.pi, -10 * np.pi])
act_max_wam_4dof = np.array([1.985, np.pi, 10 * np.pi, 10 * np.pi])
labels_4dof = ['q_1_des', 'q_3_des', 'q_dot_1_des', 'q_dot_3_des']
act_space_wam_4dof = BoxSpace(act_min_wam_4dof,
act_max_wam_4dof,
labels=labels_4dof)
# 7 DoF arm, 3 DoF actuated
init_qpos_des_7dof = np.array([0., 0.5876, 0., 1.36, 0., -0.321, -1.57])
act_min_wam_7dof = np.array(
[-1.985, -0.9, -np.pi / 2, -10 * np.pi, -10 * np.pi, -10 * np.pi])
act_max_wam_7dof = np.array(
[1.985, np.pi, np.pi / 2, 10 * np.pi, 10 * np.pi, 10 * np.pi])
labels_7dof = [
'q_1_des', 'q_3_des', 'q_5_des', 'q_dot_1_des', 'q_dot_3_des',
'q_dot_5_des'
]
act_space_wam_7dof = BoxSpace(act_min_wam_7dof,
act_max_wam_7dof,
labels=labels_7dof)
def __call__(self, dp_values: to.Tensor) -> to.Tensor:
"""
Run one rollout for every domain parameter set. The rollouts are done in segments, and after every segment the
simulation state is set to the current state in the target domain rollout.
:param dp_values: tensor containing domain parameters along the 1st dimension
:return: features computed from the time series data
"""
dp_values = to.atleast_2d(dp_values).numpy()
if self.rollouts_real is not None:
if self.use_rec_act:
# Create a policy that simply replays the recorded actions
self._set_action_field(self.rollouts_real)
policy = PlaybackPolicy(
self._env.spec,
[ro.get_data_values(self._action_field) for ro in self.rollouts_real],
no_reset=True,
)
else:
# Use the current policy to generate the actions
policy = self._policy
# The initial states will be set to states which will most likely not the be in the initial state space of
# the environment, thus we set the initial state space to an infinite space
self._env.init_space = BoxSpace(
-pyrado.inf, pyrado.inf, self._env.state_space.shape, labels=self._env.state_space.labels
)
data_sim_all = [] # for all target domain rollouts
# Iterate over domain parameter sets
for dp_value in dp_values:
data_sim_one_dp = [] # for all target domain rollouts of one domain parameter set
# Iterate over target domain rollouts
for idx_r, ro_real in enumerate(self.rollouts_real):
data_one_ro = []
ro_real.numpy()
# Split the target domain rollout if desired
if self.num_segments is not None:
segs_real = list(ro_real.split_ordered_batches(num_batches=self.num_segments))
else:
segs_real = list(ro_real.split_ordered_batches(batch_size=self.len_segments))
# Iterate over segments of one target domain rollout
cnt_step = 0
for seg_real in segs_real:
if self.use_rec_act:
# Disabled the policy reset of PlaybackPolicy to do it here manually
assert policy.no_reset
policy.curr_rec = idx_r
policy.curr_step = cnt_step
# Do the rollout for a segment
seg_sim = rollout(
self._env,
policy,
eval=True,
reset_kwargs=dict(
init_state=seg_real.states[0, :], domain_param=dict(zip(self.dp_names, dp_value))
),
stop_on_done=self.stop_on_done,
max_steps=seg_real.length,
)
check_domain_params(seg_sim, dp_value, self.dp_names)
if self.use_rec_act:
check_act_equal(seg_real, seg_sim, check_applied=self._action_field == "actions_applied")
# Pad if necessary
StepSequence.pad(seg_sim, seg_real.length)
# Increase step counter for next segment
cnt_step += seg_real.length
# Concatenate states and actions of the simulated and real segments
data_one_seg = np.concatenate(
[
seg_sim.states[: len(seg_real), :],
seg_sim.get_data_values(self._action_field)[: len(seg_real), :],
],
axis=1,
)
if self._embedding.requires_target_domain_data:
# The embedding is also using target domain data (the case for DTW distance)
data_one_seg_real = np.concatenate(
[seg_real.states[: len(seg_real), :], seg_real.get_data_values(self._action_field)],
axis=1,
)
data_one_seg = np.concatenate([data_one_seg, data_one_seg_real], axis=1)
data_one_seg = to.from_numpy(data_one_seg).to(dtype=to.get_default_dtype())
data_one_ro.append(data_one_seg)
# Append one simulated rollout
data_sim_one_dp.append(to.cat(data_one_ro, dim=0))
# Append the segments of all target domain rollouts for the current domain parameter
data_sim_all.append(to.stack(data_sim_one_dp, dim=0))
# Compute the features from all time series
data_sim_all = to.stack(data_sim_all, dim=0) # shape [batch_size, num_rollouts, len_time_series, dim_data]
data_sim_all = self._embedding(Embedding.pack(data_sim_all)) # shape [batch_size, num_rollouts * dim_data]
# Check shape
if data_sim_all.shape != (dp_values.shape[0], len(self.rollouts_real) * self._embedding.dim_output):
raise pyrado.ShapeErr(
given=data_sim_all,
expected_match=(dp_values.shape[0], len(self.rollouts_real) * self._embedding.dim_output),
)
else:
# There are no pre-recorded rollouts, e.g. during _setup_sbi().
# Use the current policy yo generate the actions.
policy = self._policy
# Do the rollouts
data_sim_all = []
for dp_value in dp_values:
ro_sim = rollout(
self._env,
policy,
eval=True,
reset_kwargs=dict(domain_param=dict(zip(self.dp_names, dp_value))),
stop_on_done=self.stop_on_done,
)
check_domain_params(ro_sim, dp_value, self.dp_names)
# Pad if necessary
StepSequence.pad(ro_sim, self._env.max_steps)
# Concatenate states and actions of the simulated segments
data_one_seg = np.concatenate(
[ro_sim.states[:-1, :], ro_sim.get_data_values(self._action_field)], axis=1
)
if self._embedding.requires_target_domain_data:
data_one_seg = np.concatenate([data_one_seg, data_one_seg], axis=1)
data_one_seg = to.from_numpy(data_one_seg).to(dtype=to.get_default_dtype())
data_sim_all.append(data_one_seg)
# Compute the features from all time series
data_sim_all = to.stack(data_sim_all, dim=0)
data_sim_all = data_sim_all.unsqueeze(1) # equivalent to only one target domain rollout
data_sim_all = self._embedding(Embedding.pack(data_sim_all)) # shape [batch_size, dim_feat]
# Check shape
if data_sim_all.shape != (dp_values.shape[0], self._embedding.dim_output):
raise pyrado.ShapeErr(
given=data_sim_all, expected_match=(dp_values.shape[0], self._embedding.dim_output)
)
return data_sim_all # shape [batch_size, num_rollouts * dim_feat]