def obtain_samples(self,
dyn_model=None,
itr=None,
policy=None,
rau=None,
delta=0,
constraint_fn=None,
constraint_cost_fn=None,
HCMPC_Activation=False,
Constrained=False):
cur_params = self.algo.policy.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_params,
max_samples=self.algo.batch_size,
dyn_model=dyn_model,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
policy=policy,
rau=rau,
delta=delta,
constraint_fn=constraint_fn,
constraint_cost_fn=constraint_cost_fn,
HCMPC_Activation=HCMPC_Activation,
Constrained=Constrained,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def test_truncate_paths():
from rllab.sampler.parallel_sampler import truncate_paths
paths = [
dict(
observations=np.zeros((100, 1)),
actions=np.zeros((100, 1)),
rewards=np.zeros(100),
env_infos=dict(),
agent_infos=dict(lala=np.zeros(100)),
),
dict(
observations=np.zeros((50, 1)),
actions=np.zeros((50, 1)),
rewards=np.zeros(50),
env_infos=dict(),
agent_infos=dict(lala=np.zeros(50)),
),
]
truncated = truncate_paths(paths, 130)
assert len(truncated) == 2
assert len(truncated[-1]["observations"]) == 30
assert len(truncated[0]["observations"]) == 100
# make sure not to change the original one
assert len(paths) == 2
assert len(paths[-1]["observations"]) == 50
def obtain_samples(self, itr):
# print("obtain samples in batch_polopt")
cur_params = self.algo.policy.get_param_values() # a list of numbers
try:
cur_low_params = self.algo.low_policy.get_param_values()
# env_params = cur_low_params if self.algo.train_low else None # need to reset low policy only when training low!
paths = parallel_sampler.sample_paths(
policy_params=cur_params,
low_policy_params=
cur_low_params, # low policy params as env params!
env_params=[self.algo.env.time_steps_agg,
self.algo], # the parameters to recover for env!
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
except AttributeError:
paths = parallel_sampler.sample_paths(
policy_params=cur_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths: # this line is run (whole path)
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
if self.algo.ma_mode == 'concurrent':
cur_policy_params = [
policy.get_param_values() for policy in self.algo.policies
]
else:
cur_policy_params = self.algo.policy.get_param_values()
if hasattr(self.algo.env, "get_param_values"):
cur_env_params = self.algo.env.get_param_values()
else:
cur_env_params = None
paths = ma_sampler.sample_paths(
policy_params=cur_policy_params,
env_params=cur_env_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
ma_mode=self.algo.ma_mode,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_params = self.policy.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_params,
max_samples=self.batch_size,
max_path_length=self.max_path_length,
)
if self.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(paths, self.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_params = self.algo.policy.get_param_values()
paths = self.sample_paths( # use the sample function above
policy_params=cur_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_pro_params = self.algo.pro_policy.get_param_values()
cur_adv_params = self.algo.adv_policy.get_param_values()
paths = parallel_sampler.sample_paths(
pro_policy_params=cur_pro_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
adv_policy_params=cur_adv_params
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_params = self.algo.policy.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_params, # TODO - can I just pass in new parameters here? (the updated ones?)
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
# TODO - does the optimizer assume that the paths came from a policy with params cur_params?
# Or can I just pass in cur_params - alpha*grads?
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_params = self.algo.policy.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_params,
max_samples=self.algo.batch_size,
include_original_frames=True,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_params = self.algo.policy.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_params, # TODO - can I just pass in new parameters here? (the updated ones?)
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
# TODO - does the optimizer assume that the paths came from a policy with params cur_params?
# Or can I just pass in cur_params - alpha*grads?
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr, determ=False):
cur_policy_params = self.algo.policy.get_param_values()
cur_env_params = self.algo.env.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_policy_params,
env_params=cur_env_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
if config.TF_NN_SETTRACE:
ipdb.set_trace()
cur_policy_params = self.algo.policy.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_policy_params,
env_params=None,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
if hasattr(self.algo.policy, 'get_param_values_with_baseline'):
cur_params = self.algo.policy.get_param_values_with_baseline()
else:
cur_params = self.algo.policy.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr, include_joint_coords=False):
# TODO: include_joint_coords not supported for BatchSampler yet.
cur_policy_params = self.algo.policy.get_param_values()
cur_env_params = self.algo.env.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_policy_params,
env_params=cur_env_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_policy_params = self.algo.policy.get_param_values()
#if hasattr(self.algo.env,"get_param_values"):
#cur_env_params = self.algo.env.get_param_values()
#else:
#cur_env_params = None
paths = parallel_sampler.sample_paths(
policy_params=cur_policy_params,
env_params=None,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_policy_params = self.algo.policy.get_param_values()
if hasattr(self.algo.env,"get_param_values"):
cur_env_params = self.algo.env.get_param_values()
else:
cur_env_params = None
paths = parallel_sampler.sample_paths(
policy_params=cur_policy_params,
env_params=cur_env_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr, target_task=None):
cur_params = self.algo.policy.get_param_values()
paths = parallel_sampler.sample_paths(
policy_params=cur_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
iter=itr,
policy=self.algo.policy,
env=self.algo.env,
baseline=self.algo.baseline,
target_task=target_task,
)
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
def obtain_samples(self, itr):
cur_params = self.algo.policy.get_param_values()
raw_paths = parallel_sampler.sample_paths(
policy_params=cur_params,
max_samples=self.algo.batch_size,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
)
if self.period is None: # hippo random p
paths = raw_paths
else:
#todo: this will break for environments where the rollout terminates after goal is reached
paths = []
for path in raw_paths:
new_length = (len(path['rewards']) //
self.period) * self.period
for key in path.keys():
if isinstance(path[key], dict):
for key2 in path[key].keys():
path[key][key2] = path[key][key2][:new_length]
else:
path[key] = path[key][:new_length]
if len(path['rewards']) > 0:
paths.append(path)
# num_padding = self.period - (len(path['rewards']) % self.period)
# for key in path.keys():
# if isinstance(path[key], dict):
# for key2 in path[key].keys():
# path[key][key2].
# paths = raw_paths
if self.algo.whole_paths:
return paths
else:
paths_truncated = parallel_sampler.truncate_paths(
paths, self.algo.batch_size)
return paths_truncated
