n_random_steps=1,
non_presampled_goal_img_is_garbage=True,
),
vae_kwargs=dict(
decoder_distribution='gaussian_identity_variance',
input_channels=3,
architecture=imsize48_default_architecture,
),
algo_kwargs=dict(lr=1e-3, ),
save_period=1,
),
)
search_space = {}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_prefix = 'dev-{}'.format(
__file__.replace('/', '-').replace('_', '-').split('.')[0])
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment_here(
skewfit_full_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
input_size=200,
output_size=32,
hidden_sizes=[64, 64]),
num_obj_network_kwargs=dict(
# num_objs: 8
input_size=8,
output_size=8,
hidden_sizes=[8]))
algo_search_space = copy.deepcopy(algo_variant)
algo_search_space = {k: [v] for k, v in algo_search_space.items()}
algo_search_space.update(
# insert sweep params here
)
env_sweeper = hyp.DeterministicHyperparameterSweeper(
env_search_space,
default_parameters=env_variant,
)
algo_sweeper = hyp.DeterministicHyperparameterSweeper(
algo_search_space,
default_parameters=algo_variant,
)
for exp_id, env_vari in enumerate(env_sweeper.iterate_hyperparameters()):
for algo_vari in algo_sweeper.iterate_hyperparameters():
variant = {'algo_kwargs': algo_vari, 'env_kwargs': env_vari}
for _ in range(n_seeds):
run_experiment(experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=use_gpu,
