def freespace(seed=4, trials=200, trial_length=50, force_gui=False):
env = PegGetter.env(p.GUI if force_gui else p.DIRECT, 0)
u_min, u_max = env.get_control_bounds()
ctrl = controller.FullRandomController(env.nu, u_min, u_max)
# use mode p.GUI to see what the trials look like
save_dir = '{}{}'.format(PegGetter.env_dir, 0)
sim = peg_in_hole.PegInHole(env,
ctrl,
num_frames=trial_length,
plot=False,
save=True,
stop_when_done=False,
save_dir=save_dir)
rand.seed(seed)
# randomly distribute data
for _ in range(trials):
seed = rand.seed()
# start at fixed location
hole, init_peg = OfflineDataCollection.random_config(env)
env.set_task_config(hole=hole, init_peg=init_peg)
ctrl = controller.FullRandomController(env.nu, u_min, u_max)
sim.ctrl = ctrl
sim.run(seed)
if sim.save:
load_data.merge_data_in_dir(cfg, save_dir, save_dir)
plt.ioff()
plt.show()
def freespace(seed_offset=0, trials=200, trial_length=50, force_gui=False):
env = PegRealGetter.env(level=0, stub=False)
u_min, u_max = env.get_control_bounds()
ctrl = controller.FullRandomController(env.nu, u_min, u_max)
# use mode p.GUI to see what the trials look like
save_dir = '{}{}'.format(PegRealGetter.env_dir, 0)
sim = peg_in_hole_real.ExperimentRunner(env,
ctrl,
num_frames=trial_length,
plot=False,
save=True,
stop_when_done=False,
save_dir=save_dir)
# randomly distribute data
for offset in range(trials):
seed = rand.seed(seed_offset + offset)
move = input('specify dx and dy to move to')
dx, dy = [float(dim) for dim in move.split()]
env.reset([dx, dy])
obs = env.state
run_name = "{}_{}_{}_{}".format(seed, obs[0].round(3),
obs[1].round(3), obs[2].round(3))
# start at fixed location
ctrl = controller.FullRandomController(env.nu, u_min, u_max)
sim.ctrl = ctrl
with peg_in_hole_real.VideoLogger():
sim.run(seed, run_name=run_name)
env.close()
if sim.save:
load_data.merge_data_in_dir(cfg, save_dir, save_dir)
plt.ioff()
plt.show()
def test_set():
# get data in and around the bug trap we want to avoid in the future
env = PegGetter.env(p.GUI, task_map['Peg-T'])
env.set_task_config(init_peg=[0.1, 0.12])
def rn(scale):
return np.random.randn() * scale
u = []
seed = rand.seed(2)
for _ in range(5):
u.append([0.4, 0.7 + rn(0.5)])
for i in range(15):
u.append([-0.0 + (i - 7) * 0.1, 0.8 + rn(0.5)])
for i in range(15):
u.append([-0.8 + rn(0.2), -0. + (i - 7) * 0.1])
for i in range(5):
u.append([-0.1 + rn(0.1), -1.])
u.append([-0.6, -0.])
for i in range(10):
u.append([-0. + rn(0.5), 0.9])
ctrl = controller.PreDeterminedController(np.array(u),
*env.get_control_bounds())
sim = peg_in_hole.PegInHole(env,
ctrl,
num_frames=len(u),
plot=False,
save=True,
stop_when_done=False)
sim.run(seed, 'peg_contact_test_set')
def freespace(seed_offset=0, trials=200, trial_length=50, force_gui=False):
env = ArmGetter.env(level=0, mode=p.GUI if force_gui else p.DIRECT)
u_min, u_max = env.get_control_bounds()
ctrl = controller.FullRandomController(env.nu, u_min, u_max)
# use mode p.GUI to see what the trials look like
save_dir = '{}{}'.format(ArmGetter.env_dir, 0)
sim = arm.ExperimentRunner(env,
ctrl,
num_frames=trial_length,
plot=False,
save=True,
stop_when_done=False,
save_dir=save_dir)
# randomly distribute data
for offset in range(trials):
seed = rand.seed(seed_offset + offset)
# random position
init = [(np.random.random() - 0.5) * 1.7,
(np.random.random() - 0.5) * 1.7,
np.random.random() * 0.5]
env.set_task_config(init=init)
ctrl = controller.FullRandomController(env.nu, u_min, u_max)
sim.ctrl = ctrl
sim.run(seed)
if sim.save:
load_data.merge_data_in_dir(cfg, save_dir, save_dir)
plt.ioff()
plt.show()
def free_space_env_init(cls, seed=1, **kwargs):
d = get_device()
env = cls.env(kwargs.pop('mode', 0), **kwargs)
ds = cls.ds(env, cls.data_dir(0), validation_ratio=0.1)
logger.info("initial random seed %d", rand.seed(seed))
return d, env, ds.current_config(), ds
def test_env_control():
init_block_pos = [-0.5, 0.1]
init_block_yaw = -math.pi / 2
face = block_push.BlockFace.LEFT
along_face = 0
# env = block_push.PushWithForceDirectlyReactionInStateEnv(dynamics_class=p.GUI, init_pusher=along_face, log_video=True,
# init_block=init_block_pos, init_yaw=init_block_yaw,
# environment_level=1)
env = block_push.PushPhysicallyAnyAlongEnv(mode=p.GUI,
log_video=True,
init_block=init_block_pos,
init_yaw=init_block_yaw,
environment_level=1)
seed = rand.seed(0)
# env.sim_step_wait = 0.01
u = []
# u.append((0, 1, 0))
# u.append((1, 1, 0))
# u.append((-1, 1, 0))
# for _ in range(80):
# u.append((0., 1, 0.))
N = 40
u_dir = np.linspace(0, -1, N)
u_mag = np.linspace(1, 0, N)
for i in range(N):
# u.append((0, 1, np.random.randn()))
# u.append((0.1, u_mag[i], u_dir[i]))
u.append((-0.5, 0.5 + np.random.rand(), 1.0))
ctrl = controller.PreDeterminedController(u)
sim = block_push.InteractivePush(env,
ctrl,
num_frames=len(u),
plot=True,
save=True)
sim.run(seed)
plt.ioff()
plt.show()
def freespace(seed_offset=0, trials=200, trial_length=50, force_gui=False):
env = GridGetter.env(level=0, check_boundaries=False)
u_min, u_max = env.get_control_bounds()
ctrl = controller.FullRandomController(env.nu, u_min, u_max)
# use mode p.GUI to see what the trials look like
save_dir = '{}{}'.format(GridGetter.env_dir, 0)
sim = gridworld.ExperimentRunner(env,
ctrl,
num_frames=trial_length,
plot=False,
save=True,
pause_s_between_steps=0.01,
stop_when_done=False,
save_dir=save_dir)
rospy.sleep(0.5)
sim.clear_markers()
# randomly distribute data
for offset in range(trials):
seed = rand.seed(seed_offset + offset)
# random position
init = [int(np.random.random() * max_val) for max_val in env.size]
env.set_task_config(init=init)
ctrl = controller.FullRandomController(env.nu, u_min, u_max)
sim.ctrl = ctrl
with recorder.WindowRecorder(
window_names=("RViz*", "RViz", "gridworld.rviz - RViz",
"gridworld.rviz* - RViz"),
name_suffix="rviz",
frame_rate=30.0,
save_dir=cfg.VIDEO_DIR):
sim.run(seed)
if sim.save:
load_data.merge_data_in_dir(cfg, save_dir, save_dir)
plt.ioff()
plt.show()
def run_controller(
default_run_prefix,
pre_run_setup,
seed=1,
level=1,
gating=None,
use_tsf=UseTsf.COORD,
nominal_adapt=OnlineAdapt.NONE,
autonomous_recovery=online_controller.AutonomousRecovery.RETURN_STATE,
use_demo=False,
use_trap_cost=True,
reuse_escape_as_demonstration=False,
num_frames=200,
run_prefix=None,
run_name=None,
assume_all_nonnominal_dynamics_are_traps=False,
rep_name=None,
visualize_rollout=False,
override_tampc_params=None,
override_mpc_params=None,
never_estimate_error=False,
apfvo_baseline=False,
apfsp_baseline=False,
**kwargs):
env = PegRealGetter.env(level=level, stub=False)
logger.info("initial random seed %d", rand.seed(seed))
ds, pm = PegRealGetter.prior(env, use_tsf, rep_name=rep_name)
dss = [ds]
demo_trajs = []
for demo in demo_trajs:
ds_local = PegRealGetter.ds(env, demo, validation_ratio=0.)
ds_local.update_preprocessor(ds.preprocessor)
dss.append(ds_local)
hybrid_dynamics = hybrid_model.HybridDynamicsModel(
dss,
pm,
env.state_difference, [use_tsf.name],
device=get_device(),
preprocessor=no_tsf_preprocessor(),
nominal_model_kwargs={'online_adapt': nominal_adapt},
local_model_kwargs=kwargs)
# we're always going to be in the nominal mode in this case; might as well speed up testing
if not use_demo and not reuse_escape_as_demonstration:
gating = AlwaysSelectNominal()
else:
gating = hybrid_dynamics.get_gating() if gating is None else gating
tampc_opts, mpc_opts = PegRealGetter.controller_options(env)
if override_tampc_params is not None:
tampc_opts.update(override_tampc_params)
if override_mpc_params is not None:
mpc_opts.update(override_mpc_params)
logger.debug(
"running with parameters\nhigh level controller: %s\nlow level MPC: %s",
pprint.pformat(tampc_opts), pprint.pformat(mpc_opts))
if apfvo_baseline or apfsp_baseline:
tampc_opts.pop('trap_cost_annealing_rate')
tampc_opts.pop('abs_unrecognized_threshold')
tampc_opts.pop('dynamics_minimum_window')
tampc_opts.pop('max_trap_weight')
if apfvo_baseline:
ctrl = online_controller.APFVO(ds,
hybrid_dynamics,
ds.original_config(),
gating=gating,
local_min_threshold=0.005,
trap_max_dist_influence=0.02,
repulsion_gain=0.01,
**tampc_opts)
if apfsp_baseline:
ctrl = online_controller.APFSP(ds,
hybrid_dynamics,
ds.original_config(),
gating=gating,
trap_max_dist_influence=0.045,
**tampc_opts)
else:
ctrl = online_controller.OnlineMPPI(
ds,
hybrid_dynamics,
ds.original_config(),
gating=gating,
autonomous_recovery=autonomous_recovery,
assume_all_nonnominal_dynamics_are_traps=
assume_all_nonnominal_dynamics_are_traps,
reuse_escape_as_demonstration=reuse_escape_as_demonstration,
never_estimate_error_dynamics=never_estimate_error,
use_trap_cost=use_trap_cost,
**tampc_opts,
mpc_opts=mpc_opts)
z = 0.98
goal = np.r_[env.hole, z, 0, 0]
ctrl.set_goal(goal)
sim = peg_in_hole_real.ExperimentRunner(env,
ctrl,
num_frames=num_frames,
plot=False,
save=True,
stop_when_done=True)
seed = rand.seed(seed)
if run_name is None:
def affix_run_name(*args):
nonlocal run_name
for token in args:
run_name += "__{}".format(token)
def get_rep_model_name(ds):
import re
tsf_name = ""
try:
for tsf in ds.preprocessor.tsf.transforms:
if isinstance(tsf, invariant.InvariantTransformer):
tsf_name = tsf.tsf.name
tsf_name = re.match(r".*?s\d+", tsf_name)[0]
except AttributeError:
pass
# TODO also include model name
return tsf_name
run_name = default_run_prefix
if apfvo_baseline:
run_prefix = 'APFVO'
elif apfsp_baseline:
run_prefix = 'APFSP'
if run_prefix is not None:
affix_run_name(run_prefix)
affix_run_name(nominal_adapt.name)
if not apfvo_baseline and not apfsp_baseline:
affix_run_name(autonomous_recovery.name +
("_WITHDEMO" if use_demo else ""))
if never_estimate_error:
affix_run_name('NO_E')
affix_run_name(level)
affix_run_name(use_tsf.name)
affix_run_name("ALLTRAP" if assume_all_nonnominal_dynamics_are_traps
else "SOMETRAP")
affix_run_name("REUSE" if reuse_escape_as_demonstration else "NOREUSE")
affix_run_name(gating.name)
affix_run_name("TRAPCOST" if use_trap_cost else "NOTRAPCOST")
affix_run_name(get_rep_model_name(ds))
affix_run_name(seed)
affix_run_name(num_frames)
time.sleep(1)
sim.clear_markers()
time.sleep(1)
sim.dd.draw_text("seed", "s{}".format(seed), 1, left_offset=-1.4)
sim.dd.draw_text("recovery_method",
"recovery {}".format(autonomous_recovery.name),
2,
left_offset=-1.4)
if reuse_escape_as_demonstration:
sim.dd.draw_text("resuse", "reuse escape", 3, left_offset=-1.4)
sim.dd.draw_text("run_name", run_name, 18, left_offset=-0.8, scale=3)
with peg_in_hole_real.VideoLogger():
pre_run_setup(env, ctrl, ds)
sim.run(seed, run_name)
logger.info("last run cost %f", np.sum(sim.last_run_cost))
time.sleep(2)
plt.ioff()
plt.show()
ENV_NAME = "Pendulum-v0"
TIMESTEPS = 15 # T
N_SAMPLES = 100 # K
ACTION_LOW = -2.0
ACTION_HIGH = 2.0
USE_PREVIOUS_TRIAL_DATA = False
SAVE_TRIAL_DATA = False
num_frames = 500
d = torch.device("cuda") if torch.cuda.is_available() else torch.device(
"cpu")
d = torch.device("cpu")
dtype = torch.double
seed = 6
logger.info("random seed %d", rand.seed(seed))
save_dir = os.path.join(cfg.DATA_DIR, ENV_NAME)
save_to = os.path.join(save_dir, "{}.mat".format(seed))
# new hyperparmaeters for approximate dynamics
TRAIN_EPOCH = 150 # need more epochs if we're freezing prior (~800)
BOOT_STRAP_ITER = 100
nx = 2
nu = 1
Q = torch.tensor([[1, 0], [0, 0.1]], dtype=dtype, device=d)
R = 0.001
config = load_data.DataConfig(predict_difference=True,
predict_all_dims=True)
# Kalman update
innovation = reward - C @ pred_mean # tilde y_k
innovation_cov = C @ pred_cov @ C.t() + obs_cov # S_k
kalman_gain = pred_cov @ C.t() @ innovation_cov.inverse() # K_k
# a posteriori estimate
self._mean = pred_mean + kalman_gain @ innovation
self._cov = pred_cov - kalman_gain @ C @ pred_cov
# fix to be symmetric
self._cov = (self._cov + self._cov.t()) * 0.5
if __name__ == "__main__":
from arm_pytorch_utilities import rand
rand.seed(0)
num_arms = 7
obs_noise = torch.ones(1) * 1
process_noise_scaling = 0.1
num_costs = 3
cost_weights = torch.rand((num_arms, num_costs))
# each arm is a row of the cost weight; normalize so it sums to 1
cost_weights /= cost_weights.sum(dim=1).view(num_arms, 1)
# give special meaning to the first few arms (they are 1-hot)
cost_weights[:num_costs, :num_costs] = torch.eye(num_costs)
print("cost weights")
print(cost_weights)
def _calculate_mab_process_noise():
def run_controller(
default_run_prefix,
pre_run_setup,
seed=1,
level=1,
gating=None,
use_tsf=UseTsf.COORD,
nominal_adapt=OnlineAdapt.NONE,
autonomous_recovery=online_controller.AutonomousRecovery.RETURN_STATE,
use_demo=False,
use_trap_cost=True,
reuse_escape_as_demonstration=False,
num_frames=200,
run_prefix=None,
run_name=None,
assume_all_nonnominal_dynamics_are_traps=False,
rep_name=None,
visualize_rollout=False,
override_tampc_params=None,
override_mpc_params=None,
**kwargs):
env = GridGetter.env(level=level)
logger.info("initial random seed %d", rand.seed(seed))
ds, pm = GridGetter.prior(env, use_tsf, rep_name=rep_name)
dss = [ds]
demo_trajs = []
for demo in demo_trajs:
ds_local = GridGetter.ds(env, demo, validation_ratio=0.)
ds_local.update_preprocessor(ds.preprocessor)
dss.append(ds_local)
hybrid_dynamics = hybrid_model.HybridDynamicsModel(
dss,
pm,
env.state_difference, [use_tsf.name],
device=get_device(),
preprocessor=no_tsf_preprocessor(),
nominal_model_kwargs={'online_adapt': nominal_adapt},
local_model_kwargs=kwargs)
# we're always going to be in the nominal mode in this case; might as well speed up testing
if not use_demo and not reuse_escape_as_demonstration:
gating = AlwaysSelectNominal()
else:
gating = hybrid_dynamics.get_gating() if gating is None else gating
tampc_opts, mpc_opts = GridGetter.controller_options(env)
if override_tampc_params is not None:
tampc_opts.update(override_tampc_params)
if override_mpc_params is not None:
mpc_opts.update(override_mpc_params)
logger.debug(
"running with parameters\nhigh level controller: %s\nlow level MPC: %s",
pprint.pformat(tampc_opts), pprint.pformat(mpc_opts))
ctrl = online_controller.OnlineMPPI(
ds,
hybrid_dynamics,
ds.original_config(),
gating=gating,
autonomous_recovery=autonomous_recovery,
assume_all_nonnominal_dynamics_are_traps=
assume_all_nonnominal_dynamics_are_traps,
reuse_escape_as_demonstration=reuse_escape_as_demonstration,
use_trap_cost=use_trap_cost,
**tampc_opts,
mpc_opts=mpc_opts)
ctrl.set_goal(env.goal)
sim = gridworld.ExperimentRunner(env,
ctrl,
num_frames=num_frames,
plot=False,
save=True,
stop_when_done=True)
seed = rand.seed(seed)
if run_name is None:
def affix_run_name(*args):
nonlocal run_name
for token in args:
run_name += "__{}".format(token)
def get_rep_model_name(ds):
import re
tsf_name = ""
try:
for tsf in ds.preprocessor.tsf.transforms:
if isinstance(tsf, invariant.InvariantTransformer):
tsf_name = tsf.tsf.name
tsf_name = re.match(r".*?s\d+", tsf_name)[0]
except AttributeError:
pass
# TODO also include model name
return tsf_name
run_name = default_run_prefix
if run_prefix is not None:
affix_run_name(run_prefix)
affix_run_name(nominal_adapt.name)
affix_run_name(autonomous_recovery.name +
("_WITHDEMO" if use_demo else ""))
affix_run_name(level)
affix_run_name(use_tsf.name)
affix_run_name("ALLTRAP" if assume_all_nonnominal_dynamics_are_traps
else "SOMETRAP")
affix_run_name("REUSE" if reuse_escape_as_demonstration else "NOREUSE")
affix_run_name(gating.name)
affix_run_name("TRAPCOST" if use_trap_cost else "NOTRAPCOST")
affix_run_name(get_rep_model_name(ds))
affix_run_name(seed)
affix_run_name(num_frames)
time.sleep(1)
sim.clear_markers()
time.sleep(1)
sim.dd.draw_text("seed", "s{}".format(seed), 1, left_offset=-1.4)
sim.dd.draw_text("recovery_method",
"recovery {}".format(autonomous_recovery.name),
2,
left_offset=-1.4)
if reuse_escape_as_demonstration:
sim.dd.draw_text("resuse", "reuse escape", 3, left_offset=-1.4)
sim.dd.draw_text("run_name", run_name, 18, left_offset=-0.8)
with recorder.WindowRecorder(window_names=("RViz*", "RViz",
"gridworld.rviz - RViz",
"gridworld.rviz* - RViz"),
name_suffix="rviz",
frame_rate=30.0,
save_dir=cfg.VIDEO_DIR):
pre_run_setup(env, ctrl, ds, sim)
sim.run(seed, run_name)
logger.info("last run cost %f", np.sum(sim.last_run_cost))
time.sleep(2)
plt.ioff()
plt.show()
def run_controller(
default_run_prefix,
pre_run_setup,
seed=1,
level=1,
gating=None,
use_tsf=UseTsf.COORD,
nominal_adapt=OnlineAdapt.NONE,
autonomous_recovery=online_controller.AutonomousRecovery.RETURN_STATE,
use_demo=False,
use_trap_cost=True,
reuse_escape_as_demonstration=False,
num_frames=200,
run_prefix=None,
run_name=None,
assume_all_nonnominal_dynamics_are_traps=False,
rep_name=None,
visualize_rollout=False,
override_tampc_params=None,
override_mpc_params=None,
never_estimate_error=False,
apfvo_baseline=False,
apfsp_baseline=False,
**kwargs):
env = PegGetter.env(p.GUI, level=level, log_video=True)
logger.info("initial random seed %d", rand.seed(seed))
ds, pm = PegGetter.prior(env, use_tsf, rep_name=rep_name)
dss = [ds]
demo_trajs = []
for demo in demo_trajs:
ds_local = PegGetter.ds(env, demo, validation_ratio=0.)
ds_local.update_preprocessor(ds.preprocessor)
dss.append(ds_local)
hybrid_dynamics = hybrid_model.HybridDynamicsModel(
dss,
pm,
env.state_difference, [use_tsf.name],
device=get_device(),
preprocessor=no_tsf_preprocessor(),
nominal_model_kwargs={'online_adapt': nominal_adapt},
local_model_kwargs=kwargs)
# we're always going to be in the nominal mode in this case; might as well speed up testing
if not use_demo and not reuse_escape_as_demonstration:
gating = AlwaysSelectNominal()
else:
gating = hybrid_dynamics.get_gating() if gating is None else gating
tampc_opts, mpc_opts = PegGetter.controller_options(env)
if override_tampc_params is not None:
tampc_opts.update(override_tampc_params)
if override_mpc_params is not None:
mpc_opts.update(override_mpc_params)
logger.debug(
"running with parameters\nhigh level controller: %s\nlow level MPC: %s",
pprint.pformat(tampc_opts), pprint.pformat(mpc_opts))
if apfvo_baseline or apfsp_baseline:
tampc_opts.pop('trap_cost_annealing_rate')
tampc_opts.pop('abs_unrecognized_threshold')
if apfvo_baseline:
rho = 0.05
if level == task_map['Peg-I']:
rho = 0.04 # use lower value to prevent obstacle detected below to prevent us from entering the goal
elif level == task_map['Peg-U']:
rho = 0.025 # use lower value to place more dense virtual obstacles to increase chance of entering
ctrl = online_controller.APFVO(ds,
hybrid_dynamics,
ds.original_config(),
gating=gating,
local_min_threshold=0.005,
trap_max_dist_influence=rho,
repulsion_gain=0.01,
**tampc_opts)
env.draw_user_text("APF-VO baseline", 13, left_offset=-1.5)
if apfsp_baseline:
# anything lower leads to oscillation between backing up and entering the trap's field of influence
rho = 0.07
if level == task_map['Peg-U']:
rho = 0.055
ctrl = online_controller.APFSP(ds,
hybrid_dynamics,
ds.original_config(),
gating=gating,
trap_max_dist_influence=rho,
backup_scale=0.7,
**tampc_opts)
env.draw_user_text("APF-SP baseline", 13, left_offset=-1.5)
else:
ctrl = online_controller.OnlineMPPI(
ds,
hybrid_dynamics,
ds.original_config(),
gating=gating,
autonomous_recovery=autonomous_recovery,
assume_all_nonnominal_dynamics_are_traps=
assume_all_nonnominal_dynamics_are_traps,
reuse_escape_as_demonstration=reuse_escape_as_demonstration,
use_trap_cost=use_trap_cost,
never_estimate_error_dynamics=never_estimate_error,
**tampc_opts,
mpc_opts=mpc_opts)
env.draw_user_text(gating.name, 13, left_offset=-1.5)
env.draw_user_text("recovery {}".format(autonomous_recovery.name),
11,
left_offset=-1.6)
if reuse_escape_as_demonstration:
env.draw_user_text("reuse escape", 10, left_offset=-1.6)
if use_trap_cost:
env.draw_user_text("trap set cost".format(
autonomous_recovery.name),
9,
left_offset=-1.6)
env.draw_user_text("run seed {}".format(seed), 12, left_offset=-1.5)
z = env.initGripperPos[2]
goal = np.r_[env.hole, z, 0, 0]
ctrl.set_goal(goal)
# env._dd.draw_point('hole', env.hole, color=(0, 0.5, 0.8))
sim = peg_in_hole.PegInHole(env,
ctrl,
num_frames=num_frames,
plot=False,
save=True,
stop_when_done=True,
visualize_rollouts=visualize_rollout)
seed = rand.seed(seed)
if run_name is None:
def affix_run_name(*args):
nonlocal run_name
for token in args:
run_name += "__{}".format(token)
def get_rep_model_name(ds):
import re
tsf_name = ""
try:
for tsf in ds.preprocessor.tsf.transforms:
if isinstance(tsf, invariant.InvariantTransformer):
tsf_name = tsf.tsf.name
tsf_name = re.match(r".*?s\d+", tsf_name)[0]
except AttributeError:
pass
return tsf_name
run_name = default_run_prefix
if apfvo_baseline:
run_prefix = 'APFVO'
elif apfsp_baseline:
run_prefix = 'APFSP'
if run_prefix is not None:
affix_run_name(run_prefix)
affix_run_name(nominal_adapt.name)
if not apfvo_baseline and not apfsp_baseline:
affix_run_name(autonomous_recovery.name +
("_WITHDEMO" if use_demo else ""))
if never_estimate_error:
affix_run_name('NO_E')
affix_run_name(level)
affix_run_name(use_tsf.name)
affix_run_name("ALLTRAP" if assume_all_nonnominal_dynamics_are_traps
else "SOMETRAP")
affix_run_name("REUSE" if reuse_escape_as_demonstration else "NOREUSE")
affix_run_name(gating.name)
affix_run_name("TRAPCOST" if use_trap_cost else "NOTRAPCOST")
affix_run_name(get_rep_model_name(ds))
affix_run_name(seed)
affix_run_name(num_frames)
env.draw_user_text(run_name, 14, left_offset=-1.5)
pre_run_setup(env, ctrl, ds)
sim.run(seed, run_name)
logger.info("last run cost %f", np.sum(sim.last_run_cost))
plt.ioff()
plt.show()
env.close()