def obtain_samples(self,
itr,
reset_args=None,
return_dict=False,
log_prefix=''):
init_policy_params_list = cur_policy_params_list = [
policy.get_param_values() for policy in self.algo.policy_list
]
if hasattr(self.algo.env, "get_param_values"):
try:
cur_env_params = self.algo.env.get_param_values()
except:
cur_env_params = None
else:
cur_env_params = None
import time
start = time.time()
if type(reset_args) != list and type(reset_args) != np.ndarray:
reset_args = [reset_args] * self.n_envs
if self.algo.policy_list[0].all_param_vals is not None:
cur_policy_params_list = [[
flatten_tensors(x.values()) for x in policy.all_param_vals
] for policy in self.algo.policy_list]
else:
cur_policy_params_list = [
[cur_policy_params] * self.n_envs
for cur_policy_params in cur_policy_params_list
]
# do tasks sequentially and parallelize within rollouts per task.
paths = {}
for n in range(len(self.algo.policy_list)):
for i in range(self.n_envs):
paths[str(n) + "_" + str(i)] = parallel_sampler.sample_paths(
policy_params=cur_policy_params_list[n][i],
env_params=cur_env_params,
max_samples=self.algo.batch_size / self.n_envs,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
reset_arg=reset_args[i],
show_prog_bar=False,
)
total_time = time.time() - start
logger.record_tabular(log_prefix + "TotalExecTime", total_time)
if not return_dict:
flatten_list = lambda l: [
item for sublist in l for item in sublist
]
paths = flatten_list(paths.values())
for n in range(len(self.algo.policy_list)):
self.algo.policy_list[n].set_param_values(
init_policy_params_list[n])
# currently don't support not whole paths (if desired, add code to truncate paths)
assert self.algo.whole_paths
return paths
def obtain_samples(self, itr, reset_args=None, return_dict=False, log_prefix=''):
init_policy_params = cur_policy_params = self.algo.policy.get_param_values()
if hasattr(self.algo.env,"get_param_values"):
try:
cur_env_params = self.algo.env.get_param_values()
except:
cur_env_params = None
else:
cur_env_params = None
import time
start = time.time()
# first, a naive implementation.
if type(reset_args) != list and type(reset_args)!=np.ndarray:
reset_args = [reset_args]*self.n_envs
if self.algo.policy.all_param_vals:
cur_policy_params = [flatten_tensors(x.values()) for x in self.algo.policy.all_param_vals]
else:
cur_policy_params = [cur_policy_params]*self.n_envs
# assume that n_envs = num parallel
if self.n_envs == parallel_sampler.singleton_pool.n_parallel:
raise NotImplementedError('this implementation is buggy.')
# 1 thread per env
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,
reset_arg=reset_args,
show_prog_bar=True,
multi_task=True,
)
else:
# do tasks sequentially and parallelize within rollouts per task.
paths = {}
for i in range(self.n_envs):
paths[i] = parallel_sampler.sample_paths(
policy_params=cur_policy_params[i],
env_params=cur_env_params,
max_samples=self.algo.batch_size / self.n_envs,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
reset_arg=reset_args[i],
show_prog_bar=False,
)
total_time = time.time() - start
logger.record_tabular(log_prefix+"TotalExecTime", total_time)
if not return_dict:
flatten_list = lambda l: [item for sublist in l for item in sublist]
paths = flatten_list(paths.values())
self.algo.policy.set_param_values(init_policy_params)
# currently don't support not whole paths (if desired, truncate paths)
assert self.algo.whole_paths
return paths
def obtain_samples(self, itr, reset_args=None, return_dict=False, log_prefix=''):
init_policy_params = cur_policy_params = self.algo.policy.get_param_values()
if hasattr(self.algo.env,"get_param_values"):
try:
cur_env_params = self.algo.env.get_param_values()
except:
cur_env_params = None
else:
cur_env_params = None
import time
start = time.time()
if type(reset_args) != list and type(reset_args)!=np.ndarray:
reset_args = [reset_args]*self.n_envs
#this following block already handled by get_param_values
if hasattr(self.algo.policy, 'all_param_vals') and self.algo.policy.all_param_vals:
cur_policy_params = [flatten_tensors(self.algo.policy.all_param_vals.values())]
else:
cur_policy_params = [cur_policy_params]*self.n_envs
# do tasks sequentially and parallelize within rollouts per task.
paths = {}
#import ipdb
#ipdb.set_trace()
for i in range(self.n_envs):
paths[i] = parallel_sampler.sample_paths(
policy_params=cur_policy_params[i],
env_params=cur_env_params,
max_samples=self.algo.batch_size / self.n_envs,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
reset_arg=reset_args[i],
show_prog_bar=False,
)
total_time = time.time() - start
logger.record_tabular(log_prefix+"TotalExecTime", total_time)
#import ipdb
#ipdb.set_trace()
if not return_dict:
flatten_list = lambda l: [item for sublist in l for item in sublist]
paths = flatten_list(paths.values())
self.algo.policy.set_param_values(init_policy_params)
# currently don't support not whole paths (if desired, add code to truncate paths)
assert self.algo.whole_paths
return paths
def obtain_samples(self, itr, reset_args=None, return_dict=False, log_prefix='',extra_input=None,extra_input_dim=None, save_img_obs=False, preupdate=True):
if extra_input is not None:
assert False, "not implemented"
if not preupdate:
assert False, "not implemented"
init_policy_params = cur_policy_params = self.algo.policy.get_param_values()
if hasattr(self.algo.env,"get_param_values"):
try:
cur_env_params = self.algo.env.get_param_values()
except:
cur_env_params = None
else:
cur_env_params = None
import time
start = time.time()
if type(reset_args) != list and type(reset_args)!=np.ndarray:
reset_args = [reset_args]*self.n_envs
if hasattr(self.algo.policy, 'all_param_vals'): #TODO: RK, need to make this less hacky and still work with non-maml policies
if self.algo.policy.all_param_vals:
cur_policy_params = [flatten_tensors(x.values()) for x in self.algo.policy.all_param_vals]
else:
cur_policy_params = [cur_policy_params]*self.n_envs
else:
cur_policy_params = [cur_policy_params]*self.n_envs
# do tasks sequentially and parallelize within rollouts per task.
paths = {}
for i in range(self.n_envs):
paths[i] = parallel_sampler.sample_paths(
policy_params=cur_policy_params[i],
env_params=cur_env_params,
max_samples=self.algo.batch_size / self.n_envs,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
reset_arg=reset_args[i],
show_prog_bar=False,
)
total_time = time.time() - start
logger.record_tabular(log_prefix+"TotalExecTime", total_time)
if not return_dict:
flatten_list = lambda l: [item for sublist in l for item in sublist]
paths = flatten_list(paths.values())
self.algo.policy.set_param_values(init_policy_params)
# currently don't support not whole paths (if desired, add code to truncate paths)
assert self.algo.whole_paths
return paths
def obtain_samples(self, itr, reset_args=None, return_dict=False, log_prefix=''):
init_policy_params = cur_policy_params = self.algo.policy.get_param_values()
if hasattr(self.algo.env,"get_param_values"):
try:
cur_env_params = self.algo.env.get_param_values()
except:
cur_env_params = None
else:
cur_env_params = None
import time
start = time.time()
if type(reset_args) != list and type(reset_args)!=np.ndarray:
reset_args = [reset_args]*self.n_envs
if self.algo.policy.all_param_vals:
cur_policy_params = [flatten_tensors(x.values()) for x in self.algo.policy.all_param_vals]
else:
cur_policy_params = [cur_policy_params]*self.n_envs
# do tasks sequentially and parallelize within rollouts per task.
paths = {}
for i in range(self.n_envs):
paths[i] = parallel_sampler.sample_paths(
policy_params=cur_policy_params[i],
env_params=cur_env_params,
max_samples=self.algo.batch_size / self.n_envs,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
reset_arg=reset_args[i],
show_prog_bar=False,
)
total_time = time.time() - start
logger.record_tabular(log_prefix+"TotalExecTime", total_time)
if not return_dict:
flatten_list = lambda l: [item for sublist in l for item in sublist]
paths = flatten_list(paths.values())
self.algo.policy.set_param_values(init_policy_params)
# currently don't support not whole paths (if desired, add code to truncate paths)
assert self.algo.whole_paths
return paths
def get_param_values(self, all_params=False, **tags):
params = self.get_params(all_params, **tags)
param_values = tf.get_default_session().run(params)
return flatten_tensors(param_values)
def get_param_values(self, **tags):
params = self.get_params(**tags)
param_values = self._sess.run(params)
return flatten_tensors(param_values)
def get_param_values(self, **tags):
params = self.get_params(**tags)
param_values = tf.get_default_session().run(params)
return flatten_tensors(param_values)
def eval_loss_grad(params):
self.policy.set_param_values(params, trainable=True)
grad = f_loss_grad(*input)
flattened_grad = tensor_utils.flatten_tensors(list(map(np.asarray, grad)))
return flattened_grad.astype(np.float64)
def get_param_values(self, **tags):
return flatten_tensors([
param.get_value(borrow=True) for param in self.get_params(**tags)
])
def get_param_values(self, **tags):
params = self.get_params(**tags)
# import ipdb; ipdb.set_trace()
param_values = tf.get_default_session().run(params)
return flatten_tensors(param_values)
def get_param_values(self, **tags):
return flatten_tensors(
[param.get_value(borrow=True)
for param in self.get_params(**tags)]
)
def eval_loss_grad(params):
self.policy.set_param_values(params, trainable=True)
grad = f_loss_grad(*input)
flattened_grad = tensor_utils.flatten_tensors(
list(map(np.asarray, grad)))
return flattened_grad.astype(np.float64)
def get_param_values(self, **tags):
if config.TF_NN_SETTRACE:
ipdb.set_trace()
params = self.get_params(**tags)
param_values = tf.get_default_session().run(params)
return flatten_tensors(param_values)
def get_param_values(self, **tags):
params = self.get_params(**tags)
# import ipdb; ipdb.set_trace()
param_values = tf.get_default_session().run(params)
return flatten_tensors(param_values)
def obtain_samples(self,
itr,
reset_args=None,
return_dict=False,
log_prefix='',
extra_input=None,
extra_input_dim=None,
save_img_obs=False,
preupdate=True,
numTrajs_perTask=None):
# if not preupdate:
# assert False, "not implemented"
init_policy_params = cur_policy_params = self.algo.policy.get_param_values(
)
if hasattr(self.algo.env, "get_param_values"):
try:
cur_env_params = self.algo.env.get_param_values()
except:
cur_env_params = None
else:
cur_env_params = None
import time
start = time.time()
if type(reset_args) != list and type(reset_args) != np.ndarray:
reset_args = [reset_args] * self.n_envs
cur_policy_params = [cur_policy_params] * self.n_envs
# do tasks sequentially and parallelize within rollouts per task.
paths = {}
all_param_vals_list = self.algo.policy.all_param_vals
if extra_input == None:
extra_infos = None
else:
assert extra_input in [
"onehot_exploration", 'gaussian_exploration', 'onehot_hacked'
]
extra_infos = [extra_input, extra_input_dim, preupdate]
for i in range(self.n_envs):
if self.algo.policy.all_param_vals is None:
policy_params = cur_policy_params[i]
else:
policy_params = flatten_tensors(
all_param_vals_list[i].values())
paths_i = parallel_sampler.sample_paths(
policy_params=policy_params,
env_params=cur_env_params,
max_samples=self.algo.batch_size / self.n_envs,
max_path_length=self.algo.max_path_length,
scope=self.algo.scope,
reset_arg=reset_args[i],
taskIdx=i,
show_prog_bar=False,
extra_infos=extra_infos)
if numTrajs_perTask != None:
paths[i] = paths_i[:numTrajs_perTask]
else:
paths[i] = paths_i
total_time = time.time() - start
logger.record_tabular(log_prefix + "TotalExecTime", total_time)
if not return_dict:
flatten_list = lambda l: [
item for sublist in l for item in sublist
]
paths = flatten_list(paths.values())
#self.algo.policy.set_param_values(init_policy_params)
# currently don't support not whole paths (if desired, add code to truncate paths)
assert self.algo.whole_paths
return paths
