def optimize_policy(self, all_samples_data, log=True):
"""
Performs MAML outer step
Args:
all_samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
meta_op_input_dict = self._extract_input_dict_meta_op(
all_samples_data, self._optimization_keys)
if log: logger.log("Optimizing")
loss_before = self.optimizer.optimize(
input_val_dict=meta_op_input_dict)
if log: logger.log("Computing statistics")
loss_after = self.optimizer.loss(input_val_dict=meta_op_input_dict)
if log:
logger.logkv('LossBefore', loss_before)
logger.logkv('LossAfter', loss_after)
def log_diagnostics(self, paths, prefix=''):
dist = [-path["env_infos"]['reward_dist'] for path in paths]
final_dist = [-path["env_infos"]['reward_dist'][-1] for path in paths]
# ctrl_cost = [-path["env_infos"]['reward_ctrl'] for path in paths]
logger.logkv(prefix + 'AvgDistance', np.mean(dist))
logger.logkv(prefix + 'AvgFinalDistance', np.mean(final_dist))
def log_diagnostics(self, paths, prefix=''):
"""
Log extra information per iteration based on the collected paths
"""
log_stds = np.vstack(
[path["agent_infos"]["log_std"] for path in paths])
logger.logkv(prefix + 'AveragePolicyStd', np.mean(np.exp(log_stds)))
def step(self):
time_step = time.time()
""" -------------------- Sampling --------------------------"""
if self.verbose:
logger.log("Policy is obtaining samples ...")
paths = self.model_sampler.obtain_samples(log=True,
log_prefix='Policy-')
""" ----------------- Processing Samples ---------------------"""
if self.verbose:
logger.log("Policy is processing samples ...")
samples_data = self.model_sample_processor.process_samples(
paths, log='all', log_prefix='Policy-')
if type(paths) is list:
self.log_diagnostics(paths, prefix='Policy-')
else:
self.log_diagnostics(sum(paths.values(), []), prefix='Policy-')
""" ------------------ Policy Update ---------------------"""
if self.verbose:
logger.log("Policy optimization...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
self.algo.optimize_policy(samples_data,
log=True,
verbose=self.verbose,
prefix='Policy-')
self.policy = self.model_sampler.policy
time_step = time.time() - time_step
logger.logkv('Policy-TimeStep', time_step)
def _synch(self, samples_data_arr, check_init=False):
time_synch = time.time()
if self.verbose:
logger.log('Model at {} is synchronizing...'.format(
self.itr_counter))
obs = np.concatenate([
samples_data['observations'] for samples_data in samples_data_arr
])
act = np.concatenate(
[samples_data['actions'] for samples_data in samples_data_arr])
obs_next = np.concatenate([
samples_data['next_observations']
for samples_data in samples_data_arr
])
self.dynamics_model.update_buffer(
obs=obs,
act=act,
obs_next=obs_next,
check_init=check_init,
)
# Reset variables for early stopping condition
self.with_new_data = True
self.remaining_model_idx = list(range(self.dynamics_model.num_models))
self.valid_loss_rolling_average = None
time_synch = time.time() - time_synch
logger.logkv('Model-TimeSynch', time_synch)
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 environment 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 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 optimize_policy(self, all_samples_data, log=True):
"""
Performs MAML outer step
Args:
all_samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
meta_op_input_dict = self._extract_input_dict_meta_op(
all_samples_data, self._optimization_keys)
logger.log("Computing KL before")
mean_kl_before = self.optimizer.constraint_val(meta_op_input_dict)
logger.log("Computing loss before")
loss_before = self.optimizer.loss(meta_op_input_dict)
logger.log("Optimizing")
self.optimizer.optimize(meta_op_input_dict)
logger.log("Computing loss after")
loss_after = self.optimizer.loss(meta_op_input_dict)
logger.log("Computing KL after")
mean_kl = self.optimizer.constraint_val(meta_op_input_dict)
if log:
logger.logkv('MeanKLBefore', mean_kl_before)
logger.logkv('MeanKL', mean_kl)
logger.logkv('LossBefore', loss_before)
logger.logkv('LossAfter', loss_after)
logger.logkv('dLoss', loss_before - loss_after)
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 obtain_samples(self, log=False, log_prefix='', buffer=None):
"""
Collect batch_size trajectories from each task
Args:
log (boolean): whether to log sampling times
log_prefix (str) : prefix for logger
random (boolean): whether the actions are random
Returns:
(dict) : A dict of paths of size [meta_batch_size] x (batch_size) x [5] x (max_path_length)
"""
# initial setup / preparation
policy = self.policy
policy.reset(dones=[True] * self.num_rollouts)
# initial reset of meta_envs
init_obses = np.array(
[self.env.reset() for _ in range(self.num_rollouts)])
sess = tf.get_default_session()
observations, actions, means, log_stds, rewards = sess.run(
[
self._observations_var, self._actions_var, self._means_var,
self._log_stds_var, self._rewards_var
],
feed_dict={self._initial_obs_ph: init_obses})
means = np.array(means).transpose((1, 0, 2))
log_stds = np.array(log_stds).transpose((1, 0, 2))
if log_stds.shape[0] == 1:
log_stds = np.repeat(log_stds, self.num_rollouts, axis=0)
agent_infos = [
dict(mean=mean, log_std=log_std)
for mean, log_std in zip(means, log_stds)
]
observations = np.array(observations).transpose((1, 0, 2))
actions = np.array(actions).transpose((1, 0, 2))
rewards = np.array(rewards).T
dones = [[False for _ in range(self.max_path_length)]
for _ in range(self.num_rollouts)]
env_infos = [dict() for _ in range(self.num_rollouts)]
paths = [
dict(observations=obs,
actions=act,
rewards=rew,
dones=done,
env_infos=env_info,
agent_infos=agent_info)
for obs, act, rew, done, env_info, agent_info in zip(
observations, actions, rewards, dones, env_infos, agent_infos)
]
self.total_timesteps_sampled += self.total_samples
logger.logkv('ModelSampler-n_timesteps', self.total_timesteps_sampled)
return paths
def log_diagnostics(self, paths, prefix=''):
reach_rew = [path["env_infos"]['reachRew'] for path in paths]
pick_rew = [path["env_infos"]['pickRew'][-1] for path in paths]
place_rew = [path["env_infos"]['placeRew'] for path in paths]
reach_dist = [path["env_infos"]['reachDist'] for path in paths]
placing_dist = [path["env_infos"]['placingDist'] for path in paths]
logger.logkv(prefix + 'AverageReachReward', np.mean(reach_rew))
logger.logkv(prefix + 'AveragePickReward', np.mean(pick_rew))
logger.logkv(prefix + 'AveragePlaceReward', np.mean(place_rew))
logger.logkv(prefix + 'AverageReachDistance', np.mean(reach_dist))
logger.logkv(prefix + 'AveragePlaceDistance', np.mean(placing_dist))
def log_diagnostics(self, paths, prefix=''):
progs = [
np.mean(path["env_infos"]["reward_forward"]) for path in paths
]
ctrl_cost = [
-np.mean(path["env_infos"]["reward_ctrl"]) for path in paths
]
logger.logkv(prefix + 'AverageForwardReturn', np.mean(progs))
logger.logkv(prefix + 'MaxForwardReturn', np.max(progs))
logger.logkv(prefix + 'MinForwardReturn', np.min(progs))
logger.logkv(prefix + 'StdForwardReturn', np.std(progs))
logger.logkv(prefix + 'AverageCtrlCost', np.mean(ctrl_cost))
def optimize_policy(self, buffer, timestep, grad_steps, log=True):
sess = tf.get_default_session()
for i in range(grad_steps):
feed_dict = create_feed_dict(placeholder_dict=self.op_phs_dict,
value_dict=buffer.random_batch(
self.sampler_batch_size))
sess.run(self.training_ops, feed_dict)
if log:
diagnostics = sess.run({**self.diagnostics_ops}, feed_dict)
for k, v in diagnostics.items():
logger.logkv(k, v)
if timestep % self.target_update_interval == 0:
self._update_target()
def _synch(self, dynamics_model_state_pickle):
time_synch = time.time()
if self.verbose:
logger.log('Policy is synchronizing...')
dynamics_model_state = pickle.loads(dynamics_model_state_pickle)
assert isinstance(dynamics_model_state, dict)
self.model_sampler.dynamics_model.set_shared_params(
dynamics_model_state)
if hasattr(self.model_sampler, 'vec_env'):
self.model_sampler.vec_env.dynamics_model.set_shared_params(
dynamics_model_state)
time_synch = time.time() - time_synch
logger.logkv('Policy-TimeSynch', time_synch)
def optimize_policy(self,
samples_data,
log=True,
prefix='',
verbose=False):
"""
Performs MAML outer step
Args:
samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
input_dict = self._extract_input_dict(samples_data,
self._optimization_keys,
prefix='train')
entropy_loss, reward_loss = self.optimizer.compute_loss_variations(
input_dict, self.entropy_loss, self.reward_loss, self.log_values)
if verbose: logger.log("Optimizing")
# Update model
loss_before = self.optimizer.optimize(input_val_dict=input_dict)
if verbose: logger.log("Computing statistics")
loss_after = self.optimizer.loss(input_val_dict=input_dict)
if log:
logger.logkv(prefix + 'Loss/LossBefore', loss_before)
logger.logkv(prefix + 'Loss/LossAfter', loss_after)
logger.logkv(prefix + 'Loss/PartialLossEntropy', entropy_loss)
logger.logkv(prefix + 'Loss/PartialLossReward', reward_loss)
def push(self):
time_push = time.time()
state_pickle = pickle.dumps(
self.dynamics_model.get_shared_param_values())
assert state_pickle is not None
while self.queue_next.qsize() > 5:
try:
logger.log('Model is off loading data from queue_next...')
_ = self.queue_next.get_nowait()
except Empty:
break
self.queue_next.put(state_pickle)
time_push = time.time() - time_push
logger.logkv('Model-TimePush', time_push)
def optimize_policy(self, all_samples_data, log=True):
"""
Performs MAML outer step
Args:
all_samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
meta_op_input_dict = self._extract_input_dict_meta_op(
all_samples_data, self._optimization_keys)
# add kl_coeffs / clip_eps to meta_op_input_dict
meta_op_input_dict['inner_kl_coeff'] = self.inner_kl_coeff
if self.clip_outer:
meta_op_input_dict['clip_eps'] = self.clip_eps
else:
meta_op_input_dict['outer_kl_coeff'] = self.outer_kl_coeff
if log: logger.log("Optimizing")
loss_before = self.optimizer.optimize(
input_val_dict=meta_op_input_dict)
if log: logger.log("Computing statistics")
loss_after, inner_kls, outer_kl = self.optimizer.compute_stats(
input_val_dict=meta_op_input_dict)
if self.adaptive_inner_kl_penalty:
if log: logger.log("Updating inner KL loss coefficients")
self.inner_kl_coeff = self.adapt_kl_coeff(self.inner_kl_coeff,
inner_kls,
self.target_inner_step)
if self.adaptive_outer_kl_penalty:
if log: logger.log("Updating outer KL loss coefficients")
self.outer_kl_coeff = self.adapt_kl_coeff(self.outer_kl_coeff,
outer_kl,
self.target_outer_step)
if log:
logger.logkv('LossBefore', loss_before)
logger.logkv('LossAfter', loss_after)
logger.logkv('KLInner', np.mean(inner_kls))
logger.logkv('KLCoeffInner', np.mean(self.inner_kl_coeff))
if not self.clip_outer: logger.logkv('KLOuter', outer_kl)
def push(self):
time_push = time.time()
policy_state_pickle = pickle.dumps(
self.policy.get_shared_param_values())
assert policy_state_pickle is not None
while self.queue_next.qsize() > 5:
try:
logger.log('Policy is off loading data from queue_next...')
_ = self.queue_next.get_nowait()
except Empty:
# very rare chance to reach here
break
self.queue_next.put(policy_state_pickle)
time_push = time.time() - time_push
logger.logkv('Policy-TimePush', time_push)
def step(self):
time_step = time.time()
''' --------------- MAML steps --------------- '''
self.policy.switch_to_pre_update() # Switch to pre-update policy
all_samples_data = []
for step in range(self.num_inner_grad_steps + 1):
if self.verbose:
logger.log("Policy Adaptation-Step %d **" % step)
""" -------------------- Sampling --------------------------"""
#time_sampling = time.time()
paths = self.model_sampler.obtain_samples(log=True,
log_prefix='Policy-',
buffer=None)
#time_sampling = time.time() - time_sampling
""" ----------------- Processing Samples ---------------------"""
#time_sample_proc = time.time()
samples_data = self.model_sample_processor.process_samples(
paths, log='all', log_prefix='Policy-')
all_samples_data.append(samples_data)
#time_sample_proc = time.time() - time_sample_proc
self.log_diagnostics(sum(list(paths.values()), []),
prefix='Policy-')
""" ------------------- Inner Policy Update --------------------"""
#time_algo_adapt = time.time()
if step < self.num_inner_grad_steps:
self.algo._adapt(samples_data)
#time_algo_adapt = time.time() - time_algo_adapt
""" ------------------ Outer Policy Update ---------------------"""
if self.verbose:
logger.log("Policy is optimizing...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
#time_algo_opt = time.time()
self.algo.optimize_policy(all_samples_data, prefix='Policy-')
#time_algo_opt = time.time() - time_algo_opt
time_step = time.time() - time_step
self.policy = self.model_sampler.policy
logger.logkv('Policy-TimeStep', time_step)
def optimize_policy(self,
samples_data,
log=True,
prefix='',
verbose=False):
"""
Performs MAML outer step
Args:
samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
input_dict = self._extract_input_dict(samples_data,
self._optimization_keys,
prefix='train')
if verbose:
logger.log("Computing KL before")
mean_kl_before = self.optimizer.constraint_val(
input_val_dict=input_dict)
if verbose:
logger.log("Computing loss before")
loss_before = self.optimizer.loss(input_val_dict=input_dict)
if verbose:
logger.log("Optimizing")
self.optimizer.optimize(input_val_dict=input_dict)
if verbose:
logger.log("Computing loss after")
loss_after = self.optimizer.loss(input_val_dict=input_dict)
if verbose:
logger.log("Computing KL after")
mean_kl = self.optimizer.constraint_val(input_val_dict=input_dict)
if log:
logger.logkv(prefix + 'MeanKLBefore', mean_kl_before)
logger.logkv(prefix + 'MeanKL', mean_kl)
logger.logkv(prefix + 'LossBefore', loss_before)
logger.logkv(prefix + 'LossAfter', loss_after)
logger.logkv(prefix + 'dLoss', loss_before - loss_after)
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 train(self):
"""
Trains policy on env using algo
"""
worker_data_queue, worker_model_queue, worker_policy_queue = self.queues
worker_data_remote, worker_model_remote, worker_policy_remote = self.remotes
for p in self.ps:
p.start()
''' --------------- worker warm-up --------------- '''
logger.log('Prepare start...')
worker_data_remote.send('prepare start')
worker_data_queue.put(self.initial_random_samples)
assert worker_data_remote.recv() == 'loop ready'
worker_model_remote.send('prepare start')
assert worker_model_remote.recv() == 'loop ready'
worker_policy_remote.send('prepare start')
assert worker_policy_remote.recv() == 'loop ready'
time_total = time.time()
''' --------------- worker looping --------------- '''
logger.log('Start looping...')
for remote in self.remotes:
remote.send('start loop')
''' --------------- collect info --------------- '''
for remote in self.remotes:
assert remote.recv() == 'loop done'
logger.log('\n------------all workers exit loops -------------')
for remote in self.remotes:
assert remote.recv() == 'worker closed'
for p in self.ps:
p.terminate()
logger.logkv('Trainer-TimeTotal', time.time() - time_total)
logger.dumpkvs()
logger.log("*****Training finished")
def process_queue(self):
do_push = 0
samples_data_arr = []
while True:
try:
if not self.remaining_model_idx:
logger.log(
'Model at iteration {} is block waiting for data'.
format(self.itr_counter))
# FIXME: check stop_cond
time_wait = time.time()
samples_data_arr_pickle = self.queue.get()
time_wait = time.time() - time_wait
logger.logkv('Model-TimeBlockWait', time_wait)
self.remaining_model_idx = list(
range(self.dynamics_model.num_models))
else:
if self.verbose:
logger.log('Model try get_nowait.........')
samples_data_arr_pickle = self.queue.get_nowait()
if samples_data_arr_pickle == 'push':
# Only push once before executing another step
if do_push == 0:
do_push = 1
self.push()
else:
samples_data_arr.extend(
pickle.loads(samples_data_arr_pickle))
except Empty:
break
do_synch = len(samples_data_arr)
if do_synch:
self._synch(samples_data_arr)
do_step = 1
if self.verbose:
logger.log(
'Model finishes processing queue with {}, {}, {}......'.format(
do_push, do_synch, do_step))
return do_push, do_synch, do_step
def step(self, obs=None, act=None, obs_next=None):
time_model_fit = time.time()
""" --------------- fit dynamics model --------------- """
if self.verbose:
logger.log(
'Model at iteration {} is training for one epoch...'.format(
self.itr_counter))
self.remaining_model_idx, self.valid_loss_rolling_average = self.dynamics_model.fit_one_epoch(
remaining_model_idx=self.remaining_model_idx,
valid_loss_rolling_average_prev=self.valid_loss_rolling_average,
with_new_data=self.with_new_data,
verbose=self.verbose,
log_tabular=True,
prefix='Model-',
)
self.with_new_data = False
time_model_fit = time.time() - time_model_fit
logger.logkv('Model-TimeStep', time_model_fit)
def optimize_supervised(self,
samples_data,
log=True,
prefix='',
verbose=False):
input_dict = self._extract_input_dict(samples_data,
self._optimization_keys,
prefix='train')
self.optimizer_s.compute_loss_variations(input_dict, None, None,
self.log_values_sup)
if verbose: logger.log("Optimizing Supervised Model")
loss_before = self.optimizer_s.optimize(input_val_dict=input_dict)
if verbose: logger.log("Computing statistics")
loss_after = self.optimizer_s.loss(input_val_dict=input_dict)
if log:
logger.logkv(prefix + 'SupervisedLossBefore', loss_before)
logger.logkv(prefix + 'SupervisedLossAfter', loss_after)
def log_diagnostics(self, paths, prefix=''):
fwrd_vel = [path["env_infos"]['reward_run'] for path in paths]
final_fwrd_vel = [path["env_infos"]['reward_run'][-1] for path in paths]
ctrl_cost = [-path["env_infos"]['reward_ctrl'] for path in paths]
logger.logkv(prefix + 'AvgForwardVel', np.mean(fwrd_vel))
logger.logkv(prefix + 'AvgFinalForwardVel', np.mean(final_fwrd_vel))
logger.logkv(prefix + 'AvgCtrlCost', np.mean(ctrl_cost))
def log_diagnostics(paths, prefix=''):
forward_vel = [
np.mean(path['env_infos']['forward_vel']) for path in paths
]
ctrl_cost = [
np.mean(path['env_infos']['control_cost']) for path in paths
]
# stability_cost = [np.mean(path['env_infos']['stability_cost']) for path in paths]
path_length = [path["observations"].shape[0] for path in paths]
logger.logkv(prefix + 'AvgForwardVel', np.mean(forward_vel))
logger.logkv(prefix + 'StdForwardVel', np.std(forward_vel))
logger.logkv(prefix + 'AvgCtrlCost', np.mean(ctrl_cost))
# logger.logkv(prefix + 'AvgStabilityCost', np.mean(stability_cost))
logger.logkv(prefix + 'AvgPathLength', np.mean(path_length))
def log_diagnostics(self, paths, prefix=''):
reach_dist = [path["env_infos"]['reachDist'] for path in paths]
placing_dist = [path["env_infos"]['placeDist'] for path in paths]
cos_dist = [path["env_infos"]['cosDist'] for path in paths]
logger.logkv(prefix + 'AverageReachDistance', np.mean(reach_dist))
logger.logkv(prefix + 'AveragePlaceDistance', np.mean(placing_dist))
logger.logkv(prefix + 'AverageCosDistance', np.mean(cos_dist))
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 run_supervised(self, policy, teacher_dict, tag):
paths = self.sampler.obtain_samples(log=False, advance_curriculum=False, policy=policy,
teacher_dict=teacher_dict, max_action=False) # TODO: consider adding a flag for max_action
samples_data = self.sample_processor.process_samples(paths, log='all', log_prefix=tag, log_teacher=self.train_with_teacher)
advance_curriculum, avg_success, avg_accuracy = self.check_advance_curriculum_rollout(samples_data)
logger.logkv(f"{tag}Advance", int(advance_curriculum))
logger.logkv(f"{tag}AvgSuccess", avg_success)
logger.logkv(f"{tag}AvgAccuracy", avg_accuracy)
return advance_curriculum
