def pre_invariant_preprocessor(use_tsf: UseTsf) -> preprocess.Transformer:
if use_tsf is UseTsf.COORD:
return preprocess.PytorchTransformer(
preprocess.NullSingleTransformer())
elif use_tsf is UseTsf.FEEDFORWARD_BASELINE:
return util.no_tsf_preprocessor()
else:
return preprocess.PytorchTransformer(
preprocess.NullSingleTransformer(),
preprocess.RobustMinMaxScaler())
def pre_invariant_preprocessor(use_tsf: UseTsf) -> preprocess.Transformer:
if use_tsf is UseTsf.COORD:
return preprocess.PytorchTransformer(
preprocess.NullSingleTransformer())
elif use_tsf in [UseTsf.SKIP, UseTsf.REX_SKIP]:
# normalize position and force dimensions separately using shared scales
return preprocess.PytorchTransformer(
preprocess.RobustMinMaxScaler(
dims_share_scale=[[0, 1], [3, 4]]),
preprocess.RobustMinMaxScaler(
dims_share_scale=[[0, 1], [3, 4]]))
else:
return preprocess.PytorchTransformer(
preprocess.NullSingleTransformer(),
preprocess.RobustMinMaxScaler(
dims_share_scale=[[0, 1], [3, 4]]))
def update_ds_with_transform(env, ds, use_tsf, get_pre_invariant_preprocessor, evaluate_transform=True,
rep_name=None):
invariant_tsf = get_transform(env, ds, use_tsf, override_name=rep_name)
if invariant_tsf:
# load transform (only 1 function for learning transform reduces potential for different learning params)
if use_tsf is not UseTsf.COORD and not invariant_tsf.load(invariant_tsf.get_last_checkpoint()):
raise RuntimeError("Transform {} should be learned before using".format(invariant_tsf.name))
if evaluate_transform:
losses = invariant_tsf.evaluate_validation(None)
logger.info("tsf on validation %s",
" ".join(
["{} {:.5f}".format(name, loss.mean().cpu().item()) if loss is not None else "" for
name, loss
in zip(invariant_tsf.loss_names(), losses)]))
components = [get_pre_invariant_preprocessor(use_tsf), invariant.InvariantTransformer(invariant_tsf)]
if use_tsf not in [UseTsf.SKIP, UseTsf.REX_SKIP]:
components.append(preprocess.PytorchTransformer(preprocess.RobustMinMaxScaler()))
preprocessor = preprocess.Compose(components)
else:
preprocessor = no_tsf_preprocessor()
# update the datasource to use transformed data
untransformed_config = ds.update_preprocessor(preprocessor)
tsf_name = use_tsf.name
if rep_name is not None:
tsf_name = "{}_{}".format(tsf_name, rep_name)
return untransformed_config, tsf_name, preprocessor
BOOT_STRAP_ITER)
new_data = np.zeros((BOOT_STRAP_ITER, nx + nu))
for i in range(BOOT_STRAP_ITER):
pre_action_state = env.state
action = np.random.uniform(low=ACTION_LOW, high=ACTION_HIGH)
env.step([action])
# env.render()
new_data[i, :nx] = pre_action_state
new_data[i, nx:] = action
fill_dataset(new_data)
logger.info("bootstrapping finished")
# TODO directly making the change in state into angular representation is wrong
preprocessor = preprocess.PytorchTransformer(
preprocess.AngleToCosSinRepresentation(0),
preprocess.AngleToCosSinRepresentation(0))
untransformed_config = ds.update_preprocessor(preprocessor)
# pm = prior.GMMPrior.from_data(ds)
# pm = prior.LSQPrior.from_data(ds)
mw = model.NetworkModelWrapper(
model.DeterministicUser(
make.make_sequential_network(config,
activation_factory=torch.nn.Tanh,
h_units=(16, 16)).to(device=d)), ds)
pm = prior.NNPrior.from_data(mw, train_epochs=0)
# linearizable_dynamics = online_model.OnlineDynamicsModel(0.1, pm, ds, sigreg=1e-10)
online_dynamics = online_model.OnlineLinearizeMixing(
0.1,
pm,
def pre_invariant_preprocessor(use_tsf: UseTsf) -> preprocess.Transformer:
return preprocess.PytorchTransformer(
preprocess.MinMaxScaler(), preprocess.NullSingleTransformer())
def no_tsf_preprocessor():
return preprocess.PytorchTransformer(preprocess.RobustMinMaxScaler())