class WorkerData(Worker):
def __init__(self, simulation_sleep):
super().__init__()
self.simulation_sleep = simulation_sleep
self.env = None
self.env_sampler = None
self.dynamics_sample_processor = None
self.samples_data_arr = []
def construct_from_feed_dict(
self,
policy_pickle,
env_pickle,
baseline_pickle, # UNUSED
dynamics_model_pickle,
feed_dict,
):
from meta_mb.samplers.sampler import Sampler
from meta_mb.samplers.mb_sample_processor import ModelSampleProcessor
env = pickle.loads(env_pickle)
policy = pickle.loads(policy_pickle)
self.env = env
self.env_sampler = Sampler(env=env,
policy=policy,
**feed_dict['sampler'])
self.dynamics_sample_processor = ModelSampleProcessor(
**feed_dict['sample_processor'])
def prepare_start(self):
random_sinusoid = self.queue.get()
self.step(random_sinusoid)
self.push()
def step(self, random_sinusoid=(False, False)):
time_step = time.time()
if self.itr_counter == 1 and self.env_sampler.policy.dynamics_model.normalization is None:
if self.verbose:
logger.log('Data starts first step...')
self.env_sampler.policy.dynamics_model = pickle.loads(
self.queue.get())
if self.verbose:
logger.log('Data first step done...')
'''------------- Obtaining samples from the environment -----------'''
if self.verbose:
logger.log("Data is obtaining samples...")
env_paths = self.env_sampler.obtain_samples(
log=True,
random=random_sinusoid[0],
sinusoid=random_sinusoid[1],
log_prefix='Data-EnvSampler-',
)
'''-------------- Processing environment samples -------------------'''
if self.verbose:
logger.log("Data is processing samples...")
samples_data = self.dynamics_sample_processor.process_samples(
env_paths,
log=True,
log_prefix='Data-EnvTrajs-',
)
self.samples_data_arr.append(samples_data)
time_step = time.time() - time_step
time_sleep = max(self.simulation_sleep - time_step, 0)
time.sleep(time_sleep)
logger.logkv('Data-TimeStep', time_step)
logger.logkv('Data-TimeSleep', time_sleep)
def _synch(self, dynamics_model_state_pickle):
time_synch = time.time()
dynamics_model_state = pickle.loads(dynamics_model_state_pickle)
assert isinstance(dynamics_model_state, dict)
self.env_sampler.policy.dynamics_model.set_shared_params(
dynamics_model_state)
time_synch = time.time() - time_synch
logger.logkv('Data-TimeSynch', time_synch)
def push(self):
time_push = time.time()
self.queue_next.put(pickle.dumps(self.samples_data_arr))
self.samples_data_arr = []
time_push = time.time() - time_push
logger.logkv('Data-TimePush', time_push)
def set_stop_cond(self):
if self.itr_counter >= self.n_itr:
self.stop_cond.set()
class WorkerData(Worker):
def __init__(self, num_rollouts_per_iter, simulation_sleep):
super().__init__()
self.num_rollouts_per_iter = num_rollouts_per_iter
self.simulation_sleep = simulation_sleep
self.env = None
self.env_sampler = None
self.dynamics_sample_processor = None
self.samples_data_arr = []
def construct_from_feed_dict(self, policy_pickle, env_pickle,
baseline_pickle, dynamics_model_pickle,
feed_dict):
from meta_mb.samplers.meta_samplers.meta_sampler import MetaSampler
from meta_mb.samplers.mb_sample_processor import ModelSampleProcessor
env = pickle.loads(env_pickle)
policy = pickle.loads(policy_pickle)
baseline = pickle.loads(baseline_pickle)
self.env = env
self.env_sampler = MetaSampler(env=env,
policy=policy,
**feed_dict['env_sampler'])
self.dynamics_sample_processor = ModelSampleProcessor(
baseline=baseline, **feed_dict['dynamics_sample_processor'])
def prepare_start(self):
initial_random_samples = self.queue.get()
self.step(initial_random_samples)
self.push()
def step(self, random=False):
time_step = time.time()
'''------------- Obtaining samples from the environment -----------'''
if self.verbose:
logger.log("Data is obtaining samples...")
env_paths = self.env_sampler.obtain_samples(
log=True,
random=random,
log_prefix='Data-EnvSampler-',
)
'''-------------- Processing environment samples -------------------'''
if self.verbose:
logger.log("Data is processing samples...")
if type(env_paths) is dict or type(env_paths) is OrderedDict:
env_paths = list(env_paths.values())
idxs = np.random.choice(range(len(env_paths)),
size=self.num_rollouts_per_iter,
replace=False)
env_paths = sum([env_paths[idx] for idx in idxs], [])
elif type(env_paths) is list:
idxs = np.random.choice(range(len(env_paths)),
size=self.num_rollouts_per_iter,
replace=False)
env_paths = [env_paths[idx] for idx in idxs]
else:
raise TypeError
samples_data = self.dynamics_sample_processor.process_samples(
env_paths,
log=True,
log_prefix='Data-EnvTrajs-',
)
self.samples_data_arr.append(samples_data)
time_step = time.time() - time_step
time_sleep = max(self.simulation_sleep - time_step, 0)
time.sleep(time_sleep)
logger.logkv('Data-TimeStep', time_step)
logger.logkv('Data-TimeSleep', time_sleep)
def _synch(self, policy_state_pickle):
time_synch = time.time()
policy_state = pickle.loads(policy_state_pickle)
assert isinstance(policy_state, dict)
self.env_sampler.policy.set_shared_params(policy_state)
time_synch = time.time() - time_synch
logger.logkv('Data-TimeSynch', time_synch)
def push(self):
time_push = time.time()
self.queue_next.put(pickle.dumps(self.samples_data_arr))
self.samples_data_arr = []
time_push = time.time() - time_push
logger.logkv('Data-TimePush', time_push)
def set_stop_cond(self):
if self.itr_counter >= self.n_itr:
self.stop_cond.set()