grad_step_size=fast_learning_rate,
hidden_nonlinearity=tf.nn.relu,
hidden_sizes=variant['hidden_sizes'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = MAMLTRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=fast_batch_size, # number of trajs for grad update
max_path_length=max_path_length,
meta_batch_size=meta_batch_size,
num_grad_updates=num_grad_updates,
n_itr=1000,
use_maml=True,
step_size=meta_step_size,
plot=False,
numExpertPolicies=20,
expertDataInfo={
'expert_loc': expertDataLoc,
'expert_itr': expertDataItr
})
algo.train()
args = dd.get_args()
experiment(args['variant'])
import doodad
try:
import cloudpickle
except ImportError as e:
raise ImportError("cloudpickle must be installed inside the docker image")
def failure():
raise ValueError("Must provide run_method via doodad args!")
fn = doodad.get_args('run_method', failure)
fn()
import doodad as dd
from mujoco_torch.doodad.launcher import run_experiment_here
args_dict = dd.get_args()
method_call = args_dict['method_call']
run_experiment_kwargs = args_dict['run_experiment_kwargs']
output_dir = args_dict['output_dir']
run_mode = args_dict.get('mode', None)
if run_mode and run_mode == 'ec2':
try:
import urllib.request
instance_id = urllib.request.urlopen(
'http://169.254.169.254/latest/meta-data/instance-id').read(
).decode()
run_experiment_kwargs['variant']['EC2_instance_id'] = instance_id
except Exception as e:
print("Could not get instance ID. Error was...")
print(e)
run_experiment_here(method_call, log_dir=output_dir, **run_experiment_kwargs)
import doodad
try:
import cloudpickle
except ImportError as e:
raise ImportError("cloudpickle must be installed inside the docker image")
def failure():
raise ValueError("Must provide run_method via doodad args!")
fn = doodad.get_args("run_method", failure)
fn()
