def main():
variant = dict(algo_kwargs=dict(
lr=1e-3,
max_timesteps=int(1E7),
buffer_size=int(5E5),
exploration_fraction=0.1,
exploration_final_eps=0.02,
print_freq=1000,
gamma=0.99,
), )
search_space = {
'algo_kwargs.prioritized_replay': [True, False],
'algo_kwargs.lr': [1e-2, 1e-3, 1e-4],
'algo_kwargs.exploration_fraction': [0.1, 0.5],
'algo_kwargs.exploration_final_eps': [0.2, 0.02],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(1):
run_experiment(
experiment,
exp_id=exp_id,
variant=variant,
exp_prefix="openai-baselines-dqn-cartpole-sweep-2",
mode='ec2',
# exp_prefix="dev-openai-baselines-dqn-cartpole-2",
# mode='local',
)
def run_algorithm(launch_settings,
env_params,
exp_prefix,
seed,
exp_id=1,
**kwargs):
"""
Launch an algorithm
:param launch_settings: See get_launch_settings_list_from_args
:param env_params: See get_env_settings
:param exp_prefix: Experiment prefix
:param seed: Experiment seed
:param exp_id: Experiment ID # to identify it later (e.g. for plotting data)
:param kwargs: Other kwargs to pass to run_experiment_lite
:return:
"""
variant = launch_settings['variant']
variant['env_params'] = env_params
variant['algo_params'] = launch_settings['algo_params']
variant['batch_norm_params'] = launch_settings['batch_norm_params']
variant['exp_id'] = exp_id
env_settings = get_env_settings(**env_params)
variant['Environment'] = env_settings['name']
algorithm_launcher = launch_settings['algorithm_launcher']
run_experiment(algorithm_launcher, exp_prefix, seed, variant, **kwargs)
def main():
n_seeds = 1
mode = "here"
exp_prefix = "dev-sl"
# n_seeds = 10
# mode = "ec2"
exp_prefix = "paper-6-14-HL-sl-H25"
H = 25
# noinspection PyTypeChecker
variant = dict(
H=H,
exp_prefix=exp_prefix,
algo_params=dict(
num_batches_per_epoch=100,
num_epochs=30,
learning_rate=1e-3,
batch_size=1000,
eval_num_episodes=64,
lstm_state_size=10,
# rnn_cell_class=LSTMCell,
# rnn_cell_params=dict(
# use_peepholes=True,
# ),
rnn_cell_class=SeparateLstmLinearCell,
rnn_cell_params=dict(
use_peepholes=True,
env_noise_std=0,
memory_noise_std=0,
output_nonlinearity=tf.nn.tanh,
# output_nonlinearity=tf.nn.softmax,
env_hidden_sizes=[],
output_dim=1,
),
softmax=False,
),
version='Supervised Learning',
env_class=HighLow,
env_params=dict(horizon=H, )
# env_class=OneCharMemory,
)
exp_id = -1
for _ in range(n_seeds):
seed = random.randint(0, 999999)
exp_id += 1
set_seed(seed)
variant['seed'] = seed
variant['exp_id'] = exp_id
run_experiment(
bptt_launcher,
exp_prefix=exp_prefix,
seed=seed,
mode=mode,
variant=variant,
exp_id=exp_id,
)
def main():
num_hyperparameters = 40
layer_norm = True
sweeper = hp.RandomHyperparameterSweeper([
hp.LogFloatParam("qf_learning_rate", 1e-5, 1e-1),
hp.LogFloatParam("policy_learning_rate", 1e-5, 1e-1),
hp.LogFloatParam("reward_scale", 10.0, 0.001),
hp.LogFloatParam("discount", 0.5, 0.99),
])
for seed in range(num_hyperparameters):
params_dict = sweeper.generate_random_hyperparameters()
variant = dict(
algo_params=dict(batch_size=128,
n_epochs=50,
epoch_length=1000,
eval_samples=1000,
replay_pool_size=1000000,
min_pool_size=256,
max_path_length=1000,
qf_weight_decay=0.00,
n_updates_per_time_step=5,
soft_target_tau=0.01,
**params_dict),
env_params=dict(
env_id='cart',
normalize_env=True,
gym_name="",
),
policy_params=dict(layer_norm=layer_norm, ),
qf_params=dict(layer_norm=layer_norm, ),
)
run_experiment(
my_ddpg_launcher,
exp_prefix="3-16-cartpole-ddpg-sweep-test",
seed=seed,
variant=variant,
mode="ec2",
)
)
# n_seeds = 1
# mode = 'local'
# exp_prefix = 'test'
n_seeds = 3
mode = 'ec2'
exp_prefix = 'sawyer_pusher_offline_ae_final'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
if variant['env_id'] == 'SawyerPushAndReachXYEnv-No-Arena-v0':
variant['train_vae_variant']['generate_vae_dataset_kwargs']['dataset_path'] = \
'datasets/SawyerPushAndReachXYEnv-No-Arena-v0_N5000_sawyer_pusher_camera_upright_v3_imsize84_random_oracle_split_0.npy'
variant['grill_variant'][
'presampled_goals_path'] = 'goals/goals_n5000_VAEWrappedEnv(ImageEnv(<SawyerPushAndReachXYEnv<SawyerPushAndReachXYEnv-No-Arena-v0>>)).npy'
else:
variant['train_vae_variant']['generate_vae_dataset_kwargs']['dataset_path'] = \
'datasets/SawyerPushAndReachXYEnv-No-Arena-v1_N5000_sawyer_pusher_camera_upright_v3_imsize84_random_oracle_split_0.npy'
variant['grill_variant'][
'presampled_goals_path'] = 'goals/goals_n5000_VAEWrappedEnv(ImageEnv(<SawyerPushAndReachXYEnv<SawyerPushAndReachXYEnv-No-Arena-v1>>)).npy'
for _ in range(n_seeds):
run_experiment(
grill_her_td3_full_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
num_exps_per_instance=2,
)
# 10,
# 100,
],
'algo_params.tdm_kwargs.max_tau': [
1,
# 15,
# 20,
],
'algo_params.supervised_weight': [
# 0,
.2,
# .4,
# .6,
# .8,
]
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
run_experiment(
experiment,
seed=np.random.randint(1, 10004),
variant=variant,
exp_id=exp_id,
# exp_prefix='tdm_rl_supervised_combo',
exp_prefix='tdm_rl_supervised_combo',
mode='local',
)
input_channels=3,
imsize=48,
architecture=architecture,
decoder_distribution='beta',
),
save_period=10,
beta=2.5,
representation_size=16,
)
search_space = {
'beta':[.5, 1, 2.5, 5]
# 'algo_kwargs.normalize_log_probs':[True],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space, default_parameters=variant,
)
for _ in range(n_seeds):
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
run_experiment(
experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=use_gpu,
num_exps_per_instance=2,
snapshot_mode='gap_and_last',
snapshot_gap=100,
# skip_wait=True,
)
search_space = {
'grill_variant.replay_buffer_kwargs.power':
[1 / 10000, 1 / 1000, 1 / 100, 1 / 70, 1 / 50],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
# n_seeds = 1
# mode = 'local'
# exp_prefix = 'test'
n_seeds = 6
mode = 'gcp'
exp_prefix = 'reacher-skew-fit-final-fixed-power-bug'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(grill_her_twin_sac_online_vae_full_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
num_exps_per_instance=2,
gcp_kwargs=dict(zone='us-west1-b',
gpu_kwargs=dict(
gpu_model='nvidia-tesla-p100',
num_gpu=1,
)))
# init_camera=sawyer_door_env_camera,
# save_video=True,
)
n_seeds = 1
mode = 'local'
exp_prefix = 'test'
# n_seeds = 3
# mode = 'ec2'
# exp_prefix = 'sawyer_door_push_and_pull_open_her_td3_full_state_reset'
search_space = {
'es_kwargs.max_sigma':[.3, .8],
'env_kwargs.num_resets_before_door_reset':[1, int(1e6)],
'env_kwargs.num_resets_before_hand_reset':[1, int(1e6)],
'env_kwargs.reset_hand_with_door':[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(
her_td3_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
exp_id=exp_id,
)
False,
],
'algo_kwargs.base_kwargs.discount': [
0.99,
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_prefix = 'dev'
# n_seeds = 5
# mode = 'ec2'
exp_prefix = 'point2d-test'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for i in range(n_seeds):
run_experiment(
tdm_twin_sac_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
time_in_mins=23 * 60,
snapshot_mode='gap_and_last',
snapshot_gap=100,
)
n_seeds = 3
# mode = 'ec2'
exp_prefix = 'online-match-hps-point2d-33x33-img-all-fc-goal00-resnet18'
search_space = {
'shared_qf_conv': [
True,
# False,
],
'collection_mode': [
# 'batch',
'online',
]
}
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,
exp_id=exp_id,
use_gpu=True,
gpu_id=0,
)
# 'MountainCar-v0',
],
'algo_class': [
DDPG,
],
# 'algo_params.use_hard_updates': [True, False],
'qf_criterion_class': [
#nn.MSELoss,
HuberLoss,
],
'algo_params.collection_mode': ['online-parallel']
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for i in range(3):
run_experiment(
experiment,
variant=variant,
exp_id=exp_id,
exp_prefix=
"DDPG-online-parallel-tests-switch-to-multiprocessing-2",
mode='local',
use_gpu=False,
# exp_prefix="double-vs-dqn-huber-sweep-cartpole",
# mode='local',
# use_gpu=True,
)
replay_buffer_size=int(2e4),
),
cnn_params=dict(
kernel_sizes=[5, 5, 3],
n_channels=[32, 32, 32],
strides=[3, 3, 2],
# pool_sizes=[1, 1, 1], this param is giving an error?
hidden_sizes=[400, 300],
paddings=[0, 0, 0],
# use_batch_norm=True, this param is giving an error?
),
qf_criterion_class=HuberLoss,
)
PARALLEL = 1
SERIES = 10
for j in range(SERIES):
for i in range(PARALLEL):
run_experiment(
experiment,
variant=variant,
exp_id=i + PARALLEL * j,
exp_prefix=
"sac-image-reacher-brandon-softlearning-hyperparameters-{0}".
format(i + PARALLEL * j),
mode='local',
skip_wait=i != PARALLEL - 1)
for launcher in [
# trpo_launcher,
# mem_trpo_launcher,
# rtrpo_launcher,
ddpg_launcher,
mem_ddpg_launcher,
rdpg_launcher,
]:
search_space = {
# 'env_class': [WaterMaze1D, WaterMazeEasy1D, WaterMazeMemory1D],
}
sweeper = DeterministicHyperparameterSweeper(
search_space, default_parameters=variant)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for seed in range(n_seeds):
exp_id += 1
set_seed(seed)
variant['seed'] = seed
variant['exp_id'] = exp_id
run_experiment(
launcher,
exp_prefix=exp_prefix,
seed=seed,
mode=mode,
variant=variant,
exp_id=exp_id,
snapshot_mode='last',
use_gpu=use_gpu,
)
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_name = 'dev-{}'.format(
__file__.replace('/', '-').replace('_', '-').split('.')[0])
n_seeds = 3
mode = 'sss'
exp_name = 'reference-skew-fit-brc-push'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
grill_her_twin_sac_online_vae_full_experiment,
unpack_variant=False,
exp_name=exp_name,
mode=mode,
variant=variant,
use_gpu=True,
num_exps_per_instance=2,
gcp_kwargs=dict(terminate=True,
zone='us-east1-c',
gpu_kwargs=dict(
gpu_model='nvidia-tesla-k80',
num_gpu=1,
)),
time_in_mins=int(2.5 * 24 * 60),
)
'grill_variant.algo_kwargs.base_kwargs.num_updates_per_env_step': [4],
'grill_variant.exploration_noise': [.3, .5],
'env_kwargs.random_init': [False],
'env_kwargs.action_scale': [.02],
'init_camera': [
sawyer_pick_and_place_camera,
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space, default_parameters=variant,
)
n_seeds = 4
mode = 'ec2'
exp_prefix = 'pickup-offline-autoencoder-grill-paper-final'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(
grill_her_td3_full_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
# trial_dir_suffix='n1000-{}--zoomed-{}'.format(n1000, zoomed),
snapshot_gap=200,
snapshot_mode='gap_and_last',
num_exps_per_instance=2,
)
save_period=10,
),
)
search_space = {}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
# n_seeds = 1
# mode = 'local'
# exp_prefix = 'test'
n_seeds = 1
mode = 'ec2'
exp_prefix = 'sawyer_xy_reacher_her_td3_state'
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=5,
)
save_video_period=1,
),
logger_config=dict(
snapshot_gap=10,
),
dump_buffer_kwargs=dict(
dump_buffer_period=50,
),
replay_buffer_size=int(5E5),
expl_path_collector_kwargs=dict(),
eval_path_collector_kwargs=dict(),
shared_qf_conv=False,
use_robot_state=False,
randomize_env=True,
batch_rl=True,
)
n_seeds = 1
mode = 'local'
mode = 'here_no_doodad'
exp_prefix = 'railrl-bear-SAC-carla-{}-{}'.format(args.env, args.obs)
run_experiment(
experiment,
exp_name=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
gpu_id=args.gpu,
unpack_variant=False,
)
],
'algo_params.soft_target_tau': [
.01,
.001,
],
'env_params.randomize_goal_on_reset': [
True,
False,
],
'net_size': [
200,
300,
400,
]
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
for variant in sweeper.iterate_hyperparameters():
exp_prefix = 'sawyer_simulated_sac_reaching_pos_cntrl'
mode = 'here_no_doodad'
for i in range(n_seeds):
run_experiment(
experiment,
mode=mode,
exp_prefix=exp_prefix,
variant=variant,
)
# 0.001,
# ],
'sac_tdm_kwargs.tdm_kwargs.sample_rollout_goals_from': [
# 'fixed',
'environment',
],
'sac_tdm_kwargs.tdm_kwargs.max_tau': [
0,
],
'sac_tdm_kwargs.base_kwargs.num_updates_per_env_step': [
1,
],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for i in range(n_seeds):
variant['multitask'] = (variant['sac_tdm_kwargs']['tdm_kwargs']
['sample_rollout_goals_from'] != 'fixed')
seed = random.randint(0, 10000)
run_experiment(
experiment,
mode=mode,
exp_prefix=exp_prefix,
seed=seed,
variant=variant,
exp_id=exp_id,
)
# 'es_params.memory_es_class': [GaussianStrategy, OUStrategy],
# 'es_params.env_es_class': [GaussianStrategy, OUStrategy],
# 'es_params.memory_es_params.max_sigma': [0.1, 0.3, 1],
# 'es_params.memory_es_params.min_sigma': [1],
# 'es_params.env_es_params.max_sigma': [0.1, 0.3, 1],
# 'es_params.env_es_params.min_sigma': [1],
# 'replay_buffer_params.keep_old_fraction': [0, 0.5, 0.9],
}
sweeper = DeterministicHyperparameterSweeper(
search_space, default_parameters=variant)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for i in range(n_seeds):
run_experiment(
get_ocm_score,
exp_prefix=exp_prefix,
seed=i,
mode=mode,
variant=variant,
exp_id=exp_id,
)
elif run_mode == 'random':
sweeper = RandomHyperparameterSweeper(
hyperparameters=[
LinearFloatParam('policy_params.rnn_cell_params.env_noise_std',
0, 1),
LinearFloatParam(
'policy_params.rnn_cell_params.memory_noise_std', 0, 1),
LogFloatParam('ddpg_params.bpt_bellman_error_weight',
1,
1001,
offset=-1),
LogFloatParam('meta_params.meta_qf_learning_rate', 1e-5, 1e-2),
def example(*_):
env = DoublePendulumEnv()
es = OUStrategy(env_spec=env.spec)
qf = FeedForwardCritic(
name_or_scope="critic",
env_spec=env.spec,
)
policy = FeedForwardPolicy(
name_or_scope="actor",
env_spec=env.spec,
)
algorithm = DDPG(
env,
es,
policy,
qf,
n_epochs=30,
batch_size=1024,
)
algorithm.train()
if __name__ == "__main__":
run_experiment(
example,
exp_prefix="ddpg-double-pendulum",
seed=0,
mode='here',
)
'grill_variant.algo_kwargs.base_kwargs.num_updates_per_env_step': [2],
'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 = 'pusher-test-pnp-merge-2'
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=2,
)
exp_prefix = "her-twin-sac-" + args.env
if len(args.label) > 0:
exp_prefix = exp_prefix + "-" + args.label
search_space = common_params
search_space.update(env_params[args.env])
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space, default_parameters=variant,
)
if args.mode == 'ec2' and args.gpu:
num_exps_per_instance = args.num_seeds
num_outer_loops = 1
else:
num_exps_per_instance = 1
num_outer_loops = args.num_seeds
for _ in range(num_outer_loops):
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
run_experiment(
grill_her_twin_sac_experiment,
exp_prefix=exp_prefix,
mode=args.mode,
exp_id=exp_id,
variant=variant,
use_gpu=args.gpu,
num_exps_per_instance=num_exps_per_instance,
snapshot_gap=int(math.ceil(variant['algo_kwargs']['base_kwargs']['num_epochs'] / 10)),
snapshot_mode='gap_and_last',
)
],
'trainer_kwargs.awr_min_q': [
True,
],
'trainer_kwargs.q_weight_decay': [0],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
# n_seeds = 1
# mode = 'local'
# exp_prefix = 'awr_sac_offline_ant_v1'
n_seeds = 2
mode = 'ec2'
exp_prefix = 'awr_sac_ant_offline_online_short_pretraining_len_v1'
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,
num_exps_per_instance=2,
use_gpu=True,
gcp_kwargs=dict(preemptible=False, ),
)
optimizer_params=dict(base_eps=1e-5, ),
policy_kwargs=dict(hidden_sizes=(100, 100), ),
multitask=False,
)
search_space = {
'env_class': [
DiscreteReacher2D,
# MountainCar,
# CartPole,
# CartPoleAngleOnly,
],
'multitask': [False, True],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
seed = random.randint(0, 999999)
run_experiment(
experiment,
exp_prefix=exp_prefix,
seed=seed,
mode=mode,
variant=variant,
use_gpu=False,
snapshot_mode='gap',
snapshot_gap=5,
)
),
algo_class=DDPG,
qf_criterion_class=HuberLoss,
)
search_space = {
# 'algo_params.use_hard_updates': [True, False],
'qf_criterion_class': [
HuberLoss,
],
'history': [2, 3]
}
# setup_logger('dqn-images-experiment', variant=variant)
# experiment(variant)
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
# for i in range(2):
run_experiment(
experiment,
variant=variant,
exp_id=exp_id,
exp_prefix="DDPG-images-pusher-partial-param-check-batch-norm",
mode='ec2',
# exp_prefix="double-vs-dqn-huber-sweep-cartpole",
# mode='local',
#use_gpu=True,
)
lr=3e-4,
normalize=True,
num_epochs=200,
weight_decay=0,
num_divisions=1,
vae=None #load vae here
)
search_space = {
'batch_size': [256],
'hidden_sizes': [[100], [100, 100], [300, 300, 300]],
'weight_decay': [.001, .01, .1],
'lr': [1e-3, 1e-4],
'normalize': [True],
}
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
for _ in range(n_seeds):
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
run_experiment(
experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=use_gpu,
snapshot_mode='gap',
snapshot_gap=20,
)
# plotter=plotter,
# render_eval_paths=True,
**variant['algo_params'])
algorithm.to(ptu.device)
algorithm.train()
if __name__ == "__main__":
# noinspection PyTypeChecker
variant = dict(algo_params=dict(
num_epochs=10,
num_steps_per_epoch=1000,
num_steps_per_eval=300,
batch_size=64,
max_path_length=30,
reward_scale=0.3,
discount=0.99,
soft_target_tau=0.001,
), )
for _ in range(1):
seed = random.randint(0, 999999)
run_experiment(
experiment,
seed=seed,
variant=variant,
exp_prefix="dev-sac-multigoal",
# exp_prefix="dev-profile",
mode='local',
use_gpu=False,
)
sweeper = hyp.DeterministicHyperparameterSweeper(
search_space,
default_parameters=variant,
)
n_seeds = 1
mode = 'local'
exp_prefix = 'dev'
n_seeds = 2
mode = 'sss'
exp_prefix = 'pusher-sf-steven-reference-script-rb-size-sweep'
for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
for _ in range(n_seeds):
run_experiment(grill_her_twin_sac_online_vae_full_experiment,
exp_prefix=exp_prefix,
mode=mode,
variant=variant,
use_gpu=True,
num_exps_per_instance=3,
time_in_mins=int(2.8 * 24 * 60),
snapshot_gap=100,
snapshot_mode='gap_and_last',
gcp_kwargs=dict(terminate=True,
zone='us-east1-c',
gpu_kwargs=dict(
gpu_model='nvidia-tesla-k80',
num_gpu=1,
)))
