random_rollout_data=True,
use_cached=False,
vae_dataset_specific_kwargs=dict(),
show=False,
),
vae_kwargs=dict(input_channels=3, ),
algo_kwargs=dict(
do_scatterplot=False,
batch_size=128,
lr=1e-3,
),
save_period=5,
),
env_class=Point2DWallEnv,
env_kwargs=dict(
render_onscreen=False,
ball_radius=1,
images_are_rgb=True,
show_goal=False,
),
algorithm='RIG',
)
run_experiment(
grill_her_td3_full_experiment,
exp_prefix='rlkit-pointmass-rig-example',
mode='here_no_doodad',
variant=variant,
# use_gpu=True, # Turn on if you have a GPU
)
num_trains_per_train_loop=100,
min_num_steps_before_training=100,
)
variant['save_video'] = True
variant['save_video_kwargs']['rows'] = 1
variant['save_video_kwargs']['save_video_period'] = 1
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
if (variant['reward_type'] == 'sparse' and
variant['dynamics_model_version'] != 'fixed_standard_laplace'):
continue
for seed in range(n_seeds):
variant['exp_id'] = exp_id
# variant['seed'] = seed
run_experiment(
probabilistic_goal_reaching_experiment,
exp_name=exp_name,
mode=mode,
variant=variant,
use_gpu=False,
num_exps_per_instance=2,
slurm_config_name='cpu_co',
# slurm_config_name='cpu_co',
gcp_kwargs=dict(zone='us-east1-c',
gpu_kwargs=dict(
gpu_model='nvidia-tesla-k80',
num_gpu=1,
)),
time_in_mins=10 * 60,
)
)
n_seeds = 1
mode = 'local'
exp_name = 'dev-{}'.format(
__file__.replace('/', '-').replace('_', '-').split('.')[0])
n_seeds = 2
mode = 'sss'
exp_name = 'pnp-img-obs-enc-d-rew-many-heads--sweep-random-init-do-not-encode-state'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for seed in range(n_seeds):
variant['exp_id'] = exp_id
# variant['seed'] = seed
run_experiment(
encoder_goal_conditioned_sac_experiment,
exp_name=exp_name,
mode=mode,
variant=variant,
use_gpu=True,
num_exps_per_instance=3,
# slurm_config_name='cpu_co',
gcp_kwargs=dict(zone='us-east1-c',
gpu_kwargs=dict(
gpu_model='nvidia-tesla-k80',
num_gpu=1,
)),
time_in_mins=int(2.5 * 24 * 60),
)
algorithm='HER-tSAC',
version='normal',
observation_key='observation',
desired_goal_key='desired_goal',
)
search_space = {}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_prefix = 'dev'
# n_seeds = 5
# mode = 'ec2'
# exp_prefix = 'fetch-push-test'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for i in range(n_seeds):
run_experiment(
relabeling_tsac_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
time_in_mins=23 * 60,
snapshot_mode='gap_and_last',
snapshot_gap=100,
)
dict(num_examples=1024, version='circle', radius=3),
# dict(num_examples=1024, version='circle', radius=0),
], ),
)
n_seeds = 1
mode = 'local'
exp_name = 'dev-{}'.format(
__file__.replace('/', '-').replace('_', '-').split('.')[0])
# n_seeds = 3
# mode = 'sss'
# exp_name = __file__.split('/')[-1].split('.')[0].replace('_', '-')
# print('exp_name', exp_name)
exp_name = 'dev-set-vae-2d-two-circles'
search_space = {}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
train_2d_set_vae,
exp_name=exp_name,
mode=mode,
variant=variant,
use_gpu=True,
)
'grill_variant.algo_kwargs.base_kwargs.num_updates_per_env_step':
[1, 2, 4, 6],
'grill_variant.algo_kwargs.base_kwargs.max_path_length': [100],
'grill_variant.algo_kwargs.online_vae_kwargs.oracle_data': [False],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_prefix = 'dev'
n_seeds = 2
mode = 'ec2'
exp_prefix = 'online-vae-pushing-parallel-sweep-NUPO'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
grill_her_td3_online_vae_full_experiment,
exp_id=exp_id,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
snapshot_gap=200,
snapshot_mode='gap_and_last',
num_exps_per_instance=1,
)
do_scatterplot=False,
batch_size=128,
lr=1e-3,
),
save_period=5,
),
env_class=Point2DWallEnv,
env_kwargs=dict(
render_onscreen=False,
ball_radius=1,
images_are_rgb=True,
show_goal=False,
),
algorithm='RIG',
)
n_seeds = 1
mode = 'here_no_doodad'
exp_prefix = 'rlkit-pointmass-rig-example'
for _ in range(n_seeds):
run_experiment(
grill_her_td3_full_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
# use_gpu=True, # Turn on if you have a GPU
)
],
'create_vae_kwargs.decoder_distribution': [
'gaussian_learned_global_image_variance',
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
beta = variant['vae_trainer_kwargs']['beta']
slw = variant['vae_trainer_kwargs']['set_loss_weight']
for _ in range(n_seeds):
variant['logger_config'] = dict(
trial_dir_suffix='beta-{}-slw{}'.format(
beta,
slw,
))
run_experiment(
train_set_vae,
variant=variant,
exp_name='vae-encoder-set-loss-sweep',
mode='sss',
# exp_name='dev-vae-encoder-sweep',
# mode='here_no_doodad',
# slurm_config_name='gpu_fc',
# slurm_config_name='gpu_low_pri',
# exp_name='vae-bernoulli-decoder',
use_gpu=True,
)
),
)
search_space = {
'env_id':['SawyerDoorHookEnv-v0', 'SawyerDoorHookResetFreeEnv-v0'],
'grill_variant.exploration_noise':[0.3, .8],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space, default_parameters=variant,
)
# n_seeds = 1
# mode = 'local'
# exp_prefix = 'test'
n_seeds = 1
mode = 'ec2'
exp_prefix = 'sawyer_door_state_her_td3'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for i in range(n_seeds):
run_experiment(
grill_her_td3_full_experiment,
exp_prefix=exp_prefix,
mode=mode,
snapshot_mode='gap_and_last',
snapshot_gap=50,
variant=variant,
use_gpu=True,
num_exps_per_instance=4,
)
'algo_params.discount': [0.99, 0.9, 0.5],
'algo_params.policy_learning_rate': [1e-4, 1e-3, 1e-2],
'algo_params.qf_learning_rate': [1e-4, 1e-3, 1e-2],
'algo_params.target_hard_update_period': [10, 100, 1000],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space, default_parameters=variant)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for i in range(n_seeds):
seed = random.randint(0, 10000)
run_experiment(
experiment,
exp_prefix=exp_prefix,
seed=seed,
mode=mode,
variant=variant,
exp_id=exp_id,
sync_s3_log=True,
sync_s3_pkl=True,
periodic_sync_interval=600,
)
if run_mode == 'random':
hyperparameters = [
hyp.LinearFloatParam('algo_params.discount', 0, 1),
hyp.LogFloatParam('algo_params.policy_learning_rate', 1e-7, 1e-1),
hyp.LogFloatParam('algo_params.qf_learning_rate', 1e-7, 1e-1),
hyp.LogIntParam('algo_params.target_hard_update_period', 1, 1000),
]
sweeper = hyp.RandomHyperparameterSweeper(
hyperparameters,
default_kwargs=variant,
Make sure to hardcode this in vae_launcher.py
ungrouped_imgs = generate_images(
env, renderer, num_images=num_ungrouped_images, set=train_sets[0])
""")
for _ in range(n_seeds):
for exp_id, variant in enumerate(variants):
variant['vae_trainer_kwargs']['beta'] = (
1. / variant['create_vae_kwargs']['latent_dim']
)
variant['vae_trainer_kwargs']['debug_bad_recons'] = (
variant['create_vae_kwargs']['decoder_distribution'] ==
'gaussian_learned_global_scalar_variance'
)
if mode == 'local':
variant['vae_algo_kwargs']['num_iters'] = 1
variant['vae_algo_kwargs']['num_epochs_per_iter'] = 1
# variant['generate_train_set_kwargs']['saved_filename'] = (
# 'manual-upload/sets/hand2xy_hand2x_1obj2xy_1obj2x_num_objs_1.pickle'
# )
run_experiment(
train_set_vae,
exp_name=exp_name,
prepend_date_to_exp_name=True,
num_exps_per_instance=2,
mode=mode,
variant=variant,
# slurm_config_name='cpu',
use_gpu=True,
# gpu_id=1,
)
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,
region='us-west-2',
num_exps_per_instance=3,
snapshot_mode='gap',
snapshot_gap=10)
# 2,
# 5,
# 10,
# ],
'vae_kwargs.weight_loss': [
True,
],
'vae_kwargs.skew_sampling': [
False,
],
'append_all_data': [
# True,
False,
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
train_from_variant,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
exp_id=exp_id,
# skip_wait=True,
use_gpu=True,
)
epoch_length=num_steps_per_iteration,
eval_samples=100,
max_path_length=H,
discount=1,
),
env_params=dict(num_steps=H,
# use_small_maze=True,
),
ou_params=dict(
max_sigma=1,
min_sigma=None,
),
exp_prefix=exp_prefix,
env_class=env_class,
version="DDPG")
exp_id = -1
for seed in range(n_seeds):
exp_id += 1
set_seed(seed)
variant['seed'] = seed
variant['exp_id'] = exp_id
run_experiment(
run_linear_ocm_exp,
exp_prefix=exp_prefix,
seed=seed,
mode=mode,
variant=variant,
exp_id=exp_id,
)
# False,
# ],
'have_no_disentangled_encoder': [
True,
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_prefix = '{}'.format(
__file__.replace('/', '-').replace('_', '-').split('.')[0])
# n_seeds = 5
# mode = 'sss'
# exp_prefix = 'disentangled-basic-test-envs-new-vae'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
disentangled_grill_her_twin_sac_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
time_in_mins=int(2.5 * 24 * 60),
)
discount=0.99,
qf_learning_rate=1e-3,
policy_learning_rate=1e-4,
),
version="DDPG",
epoch_discount_schedule_class=LinearSchedule,
epoch_discount_schedule_params=dict(
min_value=0.,
max_value=0.99,
ramp_duration=99,
),
)
for env_class in [
SwimmerEnv,
HalfCheetahEnv,
AntEnv,
HopperEnv,
]:
variant['env_class'] = env_class
variant['version'] = str(env_class)
for _ in range(5):
seed = random.randint(0, 999999)
run_experiment(
experiment,
exp_prefix="ddpg-increase-gamma",
seed=seed,
mode='ec2',
variant=variant,
use_gpu=False,
)
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_name = 'dev-{}'.format(
__file__.replace('/', '-').replace('_', '-').split('.')[0])
n_seeds = 1
mode = 'sss'
exp_name = 'one-obj-img-obs-state-reward-sweep-round3-black-background'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for seed in range(n_seeds):
variant['exp_id'] = exp_id
# variant['seed'] = seed
run_experiment(
image_based_goal_conditioned_sac_experiment,
exp_name=exp_name,
mode=mode,
variant=variant,
use_gpu=True,
num_exps_per_instance=3,
gcp_kwargs=dict(zone='us-east1-c',
gpu_kwargs=dict(
gpu_model='nvidia-tesla-k80',
num_gpu=1,
)),
time_in_mins=int(10 * 60),
)
n_seeds = 2
# mode = 'sss'
exp_prefix = 'exp2-single-set-local-mode'
search_space = {
# 'vae_algo_kwargs.num_iters': [1],
'vae_trainer_kwargs.set_loss_weight': [
0,
1,
100,
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
variants = list(sweeper.iterate_hyperparameters())
for _ in range(n_seeds):
for exp_id, variant in enumerate(variants):
variant['exp_id'] = exp_id
run_experiment(
disco_experiment,
exp_name=exp_prefix,
num_exps_per_instance=2,
mode=mode,
variant=variant,
slurm_config_name='cpu',
use_gpu=True,
gpu_id=1,
)
policy_kwargs=dict(hidden_sizes=[400, 300], ),
algorithm='SAC',
version='SAC',
env_class=HalfCheetahEnv,
)
search_space = {
'env_class': [
HalfCheetahEnv,
AntEnv,
HopperEnv,
Walker2dEnv,
],
'algo_kwargs.reward_scale': [0.1, 1, 10],
# 'algo_kwargs.num_updates_per_env_step': [1, 5],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(2):
run_experiment(
experiment,
# exp_prefix="dev-sac-sweep",
exp_prefix="sac-sweep-try-reparameterization",
mode='ec2',
exp_id=exp_id,
variant=variant,
use_gpu=False,
)
"hinge_cabinet",
# "light_switch",
],
"actor_kwargs.hidden_size": [100, 512],
"algorithm_kwargs.clip_param": [0.1, 0.2],
"actor_kwargs.hidden_activation": ["relu", "tanh"],
"use_linear_lr_decay": [True, False],
"rollout_kwargs.use_proper_time_limits": [True, False],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(args.num_seeds):
seed = random.randint(0, 100000)
variant["seed"] = seed
variant["exp_id"] = exp_id
run_experiment(
experiment,
exp_prefix=args.exp_prefix,
mode=args.mode,
variant=variant,
use_gpu=False,
snapshot_mode="none",
python_cmd=subprocess.check_output(
"which python", shell=True).decode("utf-8")[:-1],
seed=seed,
exp_id=exp_id,
)
epsilon=0.5,
tau=0.001,
),
env_params=dict(),
qf_kwargs=dict(hidden_sizes=[32, 32], ))
search_space = {
# 'env_params.num_bins': [3, 5, 10],
# 'env_params.reward_position': [False, True],
# 'algo_params.tau': [0.01, 0.001],
# 'algo_params.reward_scale': [0.1, 1, 10],
# 'algo_params.epsilon': [0.1, 0.5],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for i in range(n_seeds):
seed = random.randint(0, 10000)
run_experiment(
experiment,
seed=seed,
# exp_prefix="dqn-swimmer-sweep",
# mode='ec2',
# use_gpu=False,
exp_prefix="dev-dqn-swimmer",
mode='local',
use_gpu=True,
variant=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,
region='us-east-2',
num_exps_per_instance=1,
snapshot_mode='gap',
snapshot_gap=10,
# instance_type='c5.large',
spot_price=0.08)
init_camera=sawyer_pick_and_place_camera,
)
search_space = {}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_prefix = 'dev-{}'.format(__file__.replace('/', '-').replace('_', '-').split('.')[0])
# n_seeds = 3
# mode = 'gcp'
# exp_prefix = 'skew-fit-pickup-reference-post-refactor'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
skewfit_full_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
snapshot_gap=200,
snapshot_mode='gap_and_last',
num_exps_per_instance=3,
gcp_kwargs=dict(zone='us-west1-b', ),
)
time.sleep(1)
logger.log("end")
logger.log('Local date & time is: {}'.format(date.strftime(date_format)))
logger.log("start mujoco")
from gym.envs.mujoco import HalfCheetahEnv
e = HalfCheetahEnv()
img = e.sim.render(32, 32)
logger.log(str(sum(img)))
logger.log("end mujoco_py")
if __name__ == "__main__":
# noinspection PyTypeChecker
date_format = '%m/%d/%Y %H:%M:%S %Z'
date = datetime.now(tz=pytz.utc)
for seed in range(5):
variant = dict(
num_seconds=10,
launch_time=str(date.strftime(date_format)),
logger_config=dict(),
seed=seed,
)
run_experiment(
example,
exp_name='gcp-doodad-easy-launch-example',
mode='gcp',
variant=variant,
use_gpu=False,
)
# n_seeds = 1
# mode = 'ec2'
# exp_prefix = 'sawyer-new-pusher'
search_space = {
# 'env_kwargs.randomize_goals': [True, False],
'algo_kwargs.max_path_length': [100],
'env_kwargs.reward_info.type': [
# 'hand_to_object_only',
'shaped',
],
'exploration_type': [
'ou',
# 'epsilon',
# 'gaussian',
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
td3_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
exp_id=exp_id,
)
AntEnv,
HopperEnv,
Walker2dEnv,
],
'algo_kwargs.reward_scale': [10000, 100, 1, 0.01],
'algo_kwargs.optimizer_class': [
optim.Adam,
],
'algo_kwargs.tau': [
1e-2,
],
'algo_kwargs.num_updates_per_env_step': [
1,
],
'es_kwargs.max_sigma': [0.01, 0.1, 0.5],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(1):
run_experiment(
example,
exp_prefix="n3dpg-sweep-hard-tasks",
mode='ec2',
exp_id=exp_id,
variant=variant,
use_gpu=False,
)
0,
],
'generate_set_for_rl_kwargs.saved_filename': [
'6sets128samples_xy_x_y.pickle',
'9sets128samples_xy_x_y.pickle',
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
variants = list(sweeper.iterate_hyperparameters())
for _ in range(n_seeds):
for exp_id, variant in enumerate(variants):
if variant['generate_set_for_rl_kwargs']['saved_filename'] == (
'6sets128samples_xy_x_y'):
variant['algo_kwargs']['num_eval_steps_per_epoch'] = (6000)
else:
variant['algo_kwargs']['num_eval_steps_per_epoch'] = (9000)
run_experiment(
disco_experiment,
exp_name=exp_prefix,
prepend_date_to_exp_name=False,
num_exps_per_instance=2,
mode=mode,
variant=variant,
# slurm_config_name='cpu',
use_gpu=True,
# gpu_id=1,
)
if __name__ == "__main__":
variant = dict(vae_kwargs=dict(
imsize=64,
representation_size=128,
input_channels=3,
decoder_distribution='gaussian_identity_variance',
beta=1,
K=7,
T=10,
),
algo_kwargs=dict(
gamma=0.5,
batch_size=8,
lr=1e-4,
log_interval=0,
),
num_epochs=10000,
algorithm='VAE',
save_period=5,
physics=True)
run_experiment(
main,
exp_prefix='iodine-blocks-mpc',
mode='here_no_doodad',
variant=variant,
use_gpu=True, # Turn on if you have a GPU
)
hidden_sizes=[300, 300],
structure='norm_difference',
),
policy_kwargs=dict(hidden_sizes=[300, 300], ),
es_kwargs=dict(
theta=0.1,
max_sigma=0.1,
min_sigma=0.1,
),
qf_criterion_class=HuberLoss,
algorithm="DDPG-TDM",
)
search_space = {
'algo_kwargs.base_kwargs.num_updates_per_env_step': [1, 5, 10],
'algo_kwargs.tdm_kwargs.max_tau': [0, 5],
'env_class': [SawyerXYEnv, SawyerReachXYEnv],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
)
'obj_distance',
'obj_success',
'hand_and_obj_distance',
'hand_and_obj_success',
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_prefix = 'dev-{}'.format(
__file__.replace('/', '-').replace('_', '-').split('.')[0])
n_seeds = 3
mode = 'sss'
exp_prefix = 'her-td3-pick-reward-sweep-take2'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=False,
time_in_mins=int(2.5 * 24 * 60),
)
