def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
start_time = time.time()
rollout_time = 0
success_rate = 0
itrs_on_level = 0
for itr in range(self.start_itr, self.n_itr):
# high_entropy = False
# # At the very beginning of a new level, have high entropy
# if itrs_on_level < 3:
# high_entropy = True
# # At the very beginning, have high entropy
# if self.curriculum_step == 0 and itrs_on_level < 20:
# high_entropy = True
# # If the model never succeeds
# if itrs_on_level > 20 and success_rate < .2:
# high_entropy = True
# logger.logkv("HighE", high_entropy)
logger.logkv("ItrsOnLEvel", itrs_on_level)
itrs_on_level += 1
itr_start_time = time.time()
logger.log("\n ---------------- Iteration %d ----------------" % itr)
logger.log("Sampling set of tasks/goals for this meta-batch...")
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
samples_data, episode_logs = self.algo.collect_experiences(self.teacher_train_dict)
assert len(samples_data.action.shape) == 1, (samples_data.action.shape)
time_collection = time.time() - time_env_sampling_start
time_training_start = time.time()
# if high_entropy:
# entropy = self.algo.entropy_coef# * 10
# else:
# entropy = self.algo.entropy_coef
summary_logs = self.algo.optimize_policy(samples_data, teacher_dict=self.teacher_train_dict)
time_training = time.time() - time_training_start
self._log(episode_logs, summary_logs, tag="Train")
logger.logkv('Curriculum Step', self.curriculum_step)
advance_curriculum = self.check_advance_curriculum(episode_logs, summary_logs)
logger.logkv('Train/Advance', int(advance_curriculum))
time_env_sampling = time.time() - time_env_sampling_start
#
# """ ------------------ Reward Predictor Splicing ---------------------"""
rp_start_time = time.time()
# samples_data = self.use_reward_predictor(samples_data) # TODO: update
rp_splice_time = time.time() - rp_start_time
""" ------------------ End Reward Predictor Splicing ---------------------"""
""" ------------------ Policy Update ---------------------"""
logger.log("Optimizing policy...")
# # This needs to take all samples_data so that it can construct graph for meta-optimization.
time_rp_train_start = time.time()
# self.train_rp(samples_data)
time_rp_train = time.time() - time_rp_train_start
""" ------------------ Distillation ---------------------"""
if False: # TODO: remove False once I've checked things work with dictionaries
# if self.supervised_model is not None and advance_curriculum:
time_distill_start = time.time()
for _ in range(3): # TODO: tune this!
distill_log = self.distill(samples_data, is_training=True)
for k, v in distill_log.items():
logger.logkv(f"Distill/{k}_Train", v)
distill_time = time.time() - time_distill_start
advance_curriculum = distill_log['Accuracy'] >= self.accuracy_threshold
logger.logkv('Distill/Advance', int(advance_curriculum))
else:
distill_time = 0
""" ------------------ Policy rollouts ---------------------"""
run_policy_time = 0
if False: # TODO: remove False once I've checkeo things work with dictionaries
# if advance_curriculum or (itr % self.eval_every == 0) or (itr == self.n_itr - 1): # TODO: collect rollouts with and without the teacher
train_advance_curriculum = advance_curriculum
with torch.no_grad():
if self.supervised_model is not None:
# Distilled model
time_run_supervised_start = time.time()
self.sampler.supervised_model.reset(dones=[True] * len(samples_data.obs))
logger.log("Running supervised model")
advance_curriculum_sup = self.run_supervised(self.il_trainer.acmodel, self.no_teacher_dict, "DRollout/")
run_supervised_time = time.time() - time_run_supervised_start
else:
run_supervised_time = 0
advance_curriculum_sup = True
# Original Policy
time_run_policy_start = time.time()
self.algo.acmodel.reset(dones=[True] * len(samples_data.obs))
logger.log("Running model with teacher")
advance_curriculum_policy = self.run_supervised(self.algo.acmodel, self.teacher_train_dict, "Rollout/")
run_policy_time = time.time() - time_run_policy_start
advance_curriculum = advance_curriculum_policy and advance_curriculum_sup and train_advance_curriculum
print("ADvancing curriculum???", advance_curriculum)
logger.logkv('Advance', int(advance_curriculum))
else:
run_supervised_time = 0
# advance_curriculum = False
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps', self.sampler.total_timesteps_sampled)
logger.logkv('Time/Total', time.time() - start_time)
logger.logkv('Time/Itr', time.time() - itr_start_time)
logger.logkv('Curriculum Percent', self.curriculum_step / len(self.env.levels_list))
process = psutil.Process(os.getpid())
memory_use = process.memory_info().rss / float(2 ** 20)
logger.logkv('Memory MiB', memory_use)
logger.log(self.exp_name)
logger.logkv('Time/Sampling', time_env_sampling)
logger.logkv('Time/Training', time_training)
logger.logkv('Time/Collection', time_collection)
logger.logkv('Time/RPUse', rp_splice_time)
logger.logkv('Time/RPTrain', time_rp_train)
logger.logkv('Time/RunwTeacher', run_policy_time)
logger.logkv('Time/Distillation', distill_time)
logger.logkv('Time/RunDistilled', run_supervised_time)
logger.logkv('Time/VidRollout', rollout_time)
logger.dumpkvs()
""" ------------------ Video Saving ---------------------"""
should_save_video = (itr % self.save_videos_every == 0) or (itr == self.n_itr - 1) or advance_curriculum
if should_save_video:
# TODO: consider generating videos with every element in the powerset of feedback types
time_rollout_start = time.time()
if self.supervised_model is not None:
self.il_trainer.acmodel.reset(dones=[True])
self.save_videos(itr, self.il_trainer.acmodel, save_name='distilled_video_stoch',
num_rollouts=5,
teacher_dict=self.teacher_train_dict,
save_video=should_save_video, log_prefix="DVidRollout/Stoch", stochastic=True)
self.il_trainer.acmodel.reset(dones=[True])
self.save_videos(itr, self.il_trainer.acmodel, save_name='distilled_video_det',
num_rollouts=5,
teacher_dict=self.no_teacher_dict,
save_video=should_save_video, log_prefix="DVidRollout/Det", stochastic=False)
self.algo.acmodel.reset(dones=[True])
self.save_videos(self.curriculum_step, self.algo.acmodel, save_name='withTeacher_video_stoch',
num_rollouts=5,
teacher_dict=self.teacher_train_dict,
save_video=should_save_video, log_prefix="VidRollout/Stoch", stochastic=True)
self.algo.acmodel.reset(dones=[True])
self.save_videos(self.curriculum_step, self.algo.acmodel, save_name='withTeacher_video_det',
num_rollouts=5,
teacher_dict=self.no_teacher_dict,
save_video=should_save_video, log_prefix="VidRollout/Det", stochastic=False)
rollout_time = time.time() - time_rollout_start
else:
rollout_time = 0
params = self.get_itr_snapshot(itr)
step = self.curriculum_step
if self.log_and_save:
if (itr % self.save_every == 0) or (itr == self.n_itr - 1) or advance_curriculum:
logger.log("Saving snapshot...")
logger.save_itr_params(itr, step, params)
logger.log("Saved")
if advance_curriculum and self.advance_levels:
self.curriculum_step += 1
self.sampler.advance_curriculum()
self.algo.advance_curriculum()
# self.algo.set_optimizer()
itrs_on_level = 0
logger.log("Training finished")
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
# uninit_vars = [var for var in tf.global_variables() if not sess.run(tf.is_variable_initialized(var))]
# sess.run(tf.variables_initializer(uninit_vars))
sess.run(tf.global_variables_initializer())
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
time_env_sampling_start = time.time()
if itr == 0:
logger.log(
"Obtaining random samples from the environment...")
self.env_sampler.total_samples *= self.num_rollouts_per_iter
env_paths = self.env_sampler.obtain_samples(
log=True,
random=self.initial_random_samples,
log_prefix='Data-EnvSampler-',
verbose=True)
self.env_sampler.total_samples /= self.num_rollouts_per_iter
time_env_samp_proc = time.time()
samples_data = self.dynamics_sample_processor.process_samples(
env_paths, log=True, log_prefix='Data-EnvTrajs-')
self.env.log_diagnostics(env_paths, prefix='Data-EnvTrajs-')
logger.record_tabular('Data-TimeEnvSampleProc',
time.time() - time_env_samp_proc)
buffer = samples_data if self.sample_from_buffer else None
''' --------------- fit dynamics model --------------- '''
logger.log("Training dynamics model for %i epochs ..." %
self.dynamics_model_max_epochs)
time_fit_start = time.time()
self.dynamics_model.fit(samples_data['observations'],
samples_data['actions'],
samples_data['next_observations'],
epochs=self.dynamics_model_max_epochs,
verbose=False,
log_tabular=True,
prefix='Model-')
logger.record_tabular('Model-TimeModelFit',
time.time() - time_fit_start)
env_paths = []
for id_rollout in range(self.num_rollouts_per_iter):
times_dyn_sampling = []
times_dyn_sample_processing = []
times_optimization = []
times_step = []
grad_steps_per_rollout = self.grad_steps_per_rollout
for step in range(grad_steps_per_rollout):
# logger.log("\n ---------------- Grad-Step %d ----------------" % int(grad_steps_per_rollout*itr*self.num_rollouts_per_iter,
# + id_rollout * grad_steps_per_rollout + step))
step_start_time = time.time()
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples from the model...")
time_env_sampling_start = time.time()
paths = self.model_sampler.obtain_samples(
log=True, log_prefix='Policy-', buffer=buffer)
sampling_time = time.time() - time_env_sampling_start
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples from the model...")
time_proc_samples_start = time.time()
samples_data = self.model_sample_processor.process_samples(
paths, log='all', log_prefix='Policy-')
proc_samples_time = time.time(
) - time_proc_samples_start
if type(paths) is list:
self.log_diagnostics(paths, prefix='Policy-')
else:
self.log_diagnostics(sum(paths.values(), []),
prefix='Policy-')
""" ------------------ Policy Update ---------------------"""
logger.log("Optimizing policy...")
time_optimization_step_start = time.time()
self.algo.optimize_policy(samples_data)
optimization_time = time.time(
) - time_optimization_step_start
times_dyn_sampling.append(sampling_time)
times_dyn_sample_processing.append(proc_samples_time)
times_optimization.append(optimization_time)
times_step.append(time.time() - step_start_time)
logger.log(
"Obtaining random samples from the environment...")
env_paths.extend(
self.env_sampler.obtain_samples(
log=True,
log_prefix='Data-EnvSampler-',
verbose=True))
logger.record_tabular('Data-TimeEnvSampling',
time.time() - time_env_sampling_start)
logger.log("Processing environment samples...")
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Iteration', itr)
logger.logkv('n_timesteps',
self.env_sampler.total_timesteps_sampled)
logger.logkv('Policy-TimeSampleProc',
np.sum(times_dyn_sample_processing))
logger.logkv('Policy-TimeSampling', np.sum(times_dyn_sampling))
logger.logkv('Policy-TimeAlgoOpt', np.sum(times_optimization))
logger.logkv('Policy-TimeStep', np.sum(times_step))
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.logkv('Trainer-TimeTotal', time.time() - start_time)
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
time_env_sampling_start = time.time()
logger.log(
"Obtaining samples from the environment using the policy..."
)
env_paths = self.sampler.obtain_samples(log=True,
log_prefix='')
logger.record_tabular('Time-EnvSampling',
time.time() - time_env_sampling_start)
logger.log("Processing environment samples...")
# first processing just for logging purposes
time_env_samp_proc = time.time()
samples_data = self.sample_processor.process_samples(
env_paths, log=True, log_prefix='EnvTrajs-')
logger.record_tabular('Time-EnvSampleProc',
time.time() - time_env_samp_proc)
''' --------------- fit dynamics model --------------- '''
time_fit_start = time.time()
logger.log("Training dynamics model for %i epochs ..." %
self.dynamics_model_max_epochs)
self.dynamics_model.fit(samples_data['observations'],
samples_data['actions'],
samples_data['next_observations'],
epochs=self.dynamics_model_max_epochs,
verbose=False,
log_tabular=True,
early_stopping=True,
compute_normalization=False)
logger.log("Training the value function for %i epochs ..." %
self.vfun_max_epochs)
self.value_function.fit(samples_data['observations'],
samples_data['returns'],
epochs=self.vfun_max_epochs,
verbose=False,
log_tabular=True,
compute_normalization=False)
logger.log("Training the policy ...")
self.algo.optimize_policy(samples_data)
logger.record_tabular('Time-ModelFit',
time.time() - time_fit_start)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps',
self.sampler.total_timesteps_sampled)
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
self.log_diagnostics(env_paths, '')
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
# uninit_vars = [var for var in tf.global_variables() if not sess.run(tf.is_variable_initialized(var))]
# sess.run(tf.variables_initializer(uninit_vars))
sess.run(tf.global_variables_initializer())
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
time_env_sampling_start = time.time()
if self.initial_random_samples and itr == 0:
logger.log(
"Obtaining random samples from the environment...")
env_paths = self.env_sampler.obtain_samples(
log=True, random=True, log_prefix='Data-EnvSampler-')
else:
logger.log(
"Obtaining samples from the environment using the policy..."
)
env_paths = self.env_sampler.obtain_samples(
log=True, log_prefix='Data-EnvSampler-')
# Add sleeping time to match parallel experiment
# time.sleep(10)
logger.record_tabular('Data-TimeEnvSampling',
time.time() - time_env_sampling_start)
logger.log("Processing environment samples...")
# first processing just for logging purposes
time_env_samp_proc = time.time()
samples_data = self.dynamics_sample_processor.process_samples(
env_paths, log=True, log_prefix='Data-EnvTrajs-')
self.env.log_diagnostics(env_paths, prefix='Data-EnvTrajs-')
logger.record_tabular('Data-TimeEnvSampleProc',
time.time() - time_env_samp_proc)
''' --------------- fit dynamics model --------------- '''
time_fit_start = time.time()
self.dynamics_model.update_buffer(
samples_data['observations'],
samples_data['actions'],
samples_data['next_observations'],
check_init=True)
buffer = None if not self.sample_from_buffer else samples_data
logger.record_tabular('Model-TimeModelFit',
time.time() - time_fit_start)
''' --------------- MAML steps --------------- '''
times_dyn_sampling = []
times_dyn_sample_processing = []
times_optimization = []
times_step = []
remaining_model_idx = list(
range(self.dynamics_model.num_models))
valid_loss_rolling_average_prev = None
with_new_data = True
for id_step in range(self.repeat_steps):
for epoch in range(self.num_epochs_per_step):
logger.log(
"Training dynamics model for %i epochs ..." % 1)
remaining_model_idx, valid_loss_rolling_average = self.dynamics_model.fit_one_epoch(
remaining_model_idx,
valid_loss_rolling_average_prev,
with_new_data,
log_tabular=True,
prefix='Model-')
with_new_data = False
for step in range(self.num_grad_policy_per_step):
logger.log(
"\n ---------------- Grad-Step %d ----------------"
% int(itr * self.repeat_steps *
self.num_grad_policy_per_step + id_step *
self.num_grad_policy_per_step + step))
step_start_time = time.time()
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples from the model...")
time_env_sampling_start = time.time()
paths = self.model_sampler.obtain_samples(
log=True, log_prefix='Policy-', buffer=buffer)
sampling_time = time.time() - time_env_sampling_start
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples from the model...")
time_proc_samples_start = time.time()
samples_data = self.model_sample_processor.process_samples(
paths, log='all', log_prefix='Policy-')
proc_samples_time = time.time(
) - time_proc_samples_start
if type(paths) is list:
self.log_diagnostics(paths, prefix='Policy-')
else:
self.log_diagnostics(sum(paths.values(), []),
prefix='Policy-')
""" ------------------ Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
time_optimization_step_start = time.time()
self.algo.optimize_policy(samples_data)
optimization_time = time.time(
) - time_optimization_step_start
times_dyn_sampling.append(sampling_time)
times_dyn_sample_processing.append(proc_samples_time)
times_optimization.append(optimization_time)
times_step.append(time.time() - step_start_time)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Iteration', itr)
logger.logkv('n_timesteps',
self.env_sampler.total_timesteps_sampled)
logger.logkv('Policy-TimeSampleProc',
np.sum(times_dyn_sample_processing))
logger.logkv('Policy-TimeSampling', np.sum(times_dyn_sampling))
logger.logkv('Policy-TimeAlgoOpt', np.sum(times_optimization))
logger.logkv('Policy-TimeStep', np.sum(times_step))
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.logkv('Trainer-TimeTotal', time.time() - start_time)
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
# uninit_vars = [var for var in tf.global_variables() if not sess.run(tf.is_variable_initialized(var))]
# sess.run(tf.variables_initializer(uninit_vars))
sess.run(tf.global_variables_initializer())
if type(self.meta_steps_per_iter) is tuple:
meta_steps_per_iter = np.linspace(self.meta_steps_per_iter[0],
self.meta_steps_per_iter[1],
self.n_itr).astype(np.int)
else:
meta_steps_per_iter = [self.meta_steps_per_iter] * self.n_itr
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
time_env_sampling_start = time.time()
if self.initial_random_samples and itr == 0:
logger.log(
"Obtaining random samples from the environment...")
env_paths = self.env_sampler.obtain_samples(
log=True, random=True, log_prefix='EnvSampler-')
else:
logger.log(
"Obtaining samples from the environment using the policy..."
)
env_paths = self.env_sampler.obtain_samples(
log=True, log_prefix='EnvSampler-')
logger.record_tabular('Time-EnvSampling',
time.time() - time_env_sampling_start)
logger.log("Processing environment samples...")
# first processing just for logging purposes
time_env_samp_proc = time.time()
if type(env_paths) is dict or type(
env_paths) is collections.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='EnvTrajs-')
self.env.log_diagnostics(env_paths, prefix='EnvTrajs-')
logger.record_tabular('Time-EnvSampleProc',
time.time() - time_env_samp_proc)
logger.record_tabular('Time-Data',
time.time() - time_env_sampling_start)
''' --------------- fit dynamics model --------------- '''
time_fit_start = time.time()
logger.log("Training dynamics model for %i epochs ..." %
(self.dynamics_model_max_epochs))
self.dynamics_model.fit(samples_data['observations'],
samples_data['actions'],
samples_data['next_observations'],
epochs=self.dynamics_model_max_epochs,
verbose=True,
log_tabular=True)
buffer = None if not self.sample_from_buffer else samples_data
logger.record_tabular('Time-ModelFit',
time.time() - time_fit_start)
''' ------------ log real performance --------------- '''
if self.log_real_performance:
logger.log("Evaluating the performance of the real policy")
self.policy.switch_to_pre_update()
env_paths = self.env_sampler.obtain_samples(
log=True, log_prefix='PrePolicy-')
samples_data = self.model_sample_processor.process_samples(
env_paths, log='all', log_prefix='PrePolicy-')
self.algo._adapt(samples_data)
env_paths = self.env_sampler.obtain_samples(
log=True, log_prefix='PostPolicy-')
self.model_sample_processor.process_samples(
env_paths, log='all', log_prefix='PostPolicy-')
''' --------------- MAML steps --------------- '''
times_dyn_sampling = []
times_dyn_sample_processing = []
times_meta_sampling = []
times_inner_step = []
times_total_inner_step = []
times_outer_step = []
times_maml_steps = []
for meta_itr in range(meta_steps_per_iter[itr]):
logger.log(
"\n ---------------- Meta-Step %d ----------------" %
int(sum(meta_steps_per_iter[:itr]) + meta_itr))
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
all_samples_data, all_paths = [], []
list_sampling_time, list_inner_step_time, list_outer_step_time, list_proc_samples_time = [], [], [], []
time_maml_steps_start = time.time()
start_total_inner_time = time.time()
for step in range(self.num_inner_grad_steps + 1):
logger.log("\n ** Adaptation-Step %d **" % step)
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
paths = self.model_sampler.obtain_samples(
log=True,
log_prefix='Step_%d-' % step,
buffer=buffer)
list_sampling_time.append(time.time() -
time_env_sampling_start)
all_paths.append(paths)
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples...")
time_proc_samples_start = time.time()
samples_data = self.model_sample_processor.process_samples(
paths, log='all', log_prefix='Step_%d-' % step)
all_samples_data.append(samples_data)
list_proc_samples_time.append(time.time() -
time_proc_samples_start)
self.log_diagnostics(sum(list(paths.values()), []),
prefix='Step_%d-' % step)
""" ------------------- Inner Policy Update --------------------"""
time_inner_step_start = time.time()
if step < self.num_inner_grad_steps:
logger.log("Computing inner policy updates...")
self.algo._adapt(samples_data)
list_inner_step_time.append(time.time() -
time_inner_step_start)
total_inner_time = time.time() - start_total_inner_time
time_maml_opt_start = time.time()
""" ------------------ Outer Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
time_outer_step_start = time.time()
self.algo.optimize_policy(all_samples_data)
times_inner_step.append(list_inner_step_time)
times_total_inner_step.append(total_inner_time)
times_outer_step.append(time.time() -
time_outer_step_start)
times_meta_sampling.append(np.sum(list_sampling_time))
times_dyn_sampling.append(list_sampling_time)
times_dyn_sample_processing.append(list_proc_samples_time)
times_maml_steps.append(time.time() -
time_maml_steps_start)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
if self.log_real_performance:
logger.logkv(
'n_timesteps',
self.env_sampler.total_timesteps_sampled /
(3 * self.policy.meta_batch_size) *
self.num_rollouts_per_iter)
else:
logger.logkv(
'n_timesteps',
self.env_sampler.total_timesteps_sampled /
self.policy.meta_batch_size *
self.num_rollouts_per_iter)
logger.logkv('AvgTime-OuterStep', np.mean(times_outer_step))
logger.logkv('AvgTime-InnerStep', np.mean(times_inner_step))
logger.logkv('AvgTime-TotalInner',
np.mean(times_total_inner_step))
logger.logkv('AvgTime-InnerStep', np.mean(times_inner_step))
logger.logkv('AvgTime-SampleProc',
np.mean(times_dyn_sample_processing))
logger.logkv('AvgTime-Sampling', np.mean(times_dyn_sampling))
logger.logkv('AvgTime-MAMLSteps', np.mean(times_maml_steps))
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
self.sampler.update_tasks()
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
logger.log(
"Sampling set of tasks/goals for this meta-batch...")
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
paths = self.sampler.obtain_samples(log=True,
log_prefix='train-')
sampling_time = time.time() - time_env_sampling_start
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples...")
time_proc_samples_start = time.time()
samples_data = self.sample_processor.process_samples(
paths, log='all', log_prefix='train-')
proc_samples_time = time.time() - time_proc_samples_start
if type(paths) is list:
self.log_diagnostics(paths, prefix='train-')
else:
self.log_diagnostics(sum(paths.values(), []),
prefix='train-')
""" ------------------ Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
time_optimization_step_start = time.time()
self.algo.optimize_policy(samples_data)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps',
self.sampler.total_timesteps_sampled)
logger.logkv('Time-Optimization',
time.time() - time_optimization_step_start)
logger.logkv('Time-SampleProc', np.sum(proc_samples_time))
logger.logkv('Time-Sampling', sampling_time)
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
logger.log(
"Sampling set of tasks/goals for this meta-batch...")
self.sampler.update_tasks()
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
all_samples_data, all_paths = [], []
list_sampling_time, list_inner_step_time, list_outer_step_time, list_proc_samples_time = [], [], [], []
start_total_inner_time = time.time()
for step in range(self.num_inner_grad_steps + 1):
logger.log('** Step ' + str(step) + ' **')
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
paths = self.sampler.obtain_samples(log=True,
log_prefix='Step_%d-' %
step)
list_sampling_time.append(time.time() -
time_env_sampling_start)
all_paths.append(paths)
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples...")
time_proc_samples_start = time.time()
samples_data = self.sample_processor.process_samples(
paths, log='all', log_prefix='Step_%d-' % step)
all_samples_data.append(samples_data)
list_proc_samples_time.append(time.time() -
time_proc_samples_start)
self.log_diagnostics(sum(list(paths.values()), []),
prefix='Step_%d-' % step)
""" ------------------- Inner Policy Update --------------------"""
time_inner_step_start = time.time()
if step < self.num_inner_grad_steps:
logger.log("Computing inner policy updates...")
self.algo._adapt(samples_data)
# train_writer = tf.summary.FileWriter('/home/ignasi/Desktop/maml_zoo_graph',
# sess.graph)
list_inner_step_time.append(time.time() -
time_inner_step_start)
total_inner_time = time.time() - start_total_inner_time
time_maml_opt_start = time.time()
""" ------------------ Outer Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
time_outer_step_start = time.time()
self.algo.optimize_policy(all_samples_data)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps',
self.sampler.total_timesteps_sampled)
logger.logkv('Time-OuterStep',
time.time() - time_outer_step_start)
logger.logkv('Time-TotalInner', total_inner_time)
logger.logkv('Time-InnerStep', np.sum(list_inner_step_time))
logger.logkv('Time-SampleProc', np.sum(list_proc_samples_time))
logger.logkv('Time-Sampling', np.sum(list_sampling_time))
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.logkv('Time-MAMLSteps',
time.time() - time_maml_opt_start)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
if type(self.steps_per_iter) is tuple:
steps_per_iter = np.linspace(self.steps_per_iter[0],
self.steps_per_iter[1],
self.n_itr).astype(np.int)
else:
steps_per_iter = [self.steps_per_iter] * self.n_itr
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
time_env_sampling_start = time.time()
if self.initial_random_samples and itr == 0:
logger.log(
"Obtaining random samples from the environment...")
env_paths = self.env_sampler.obtain_samples(
log=True, random=True, log_prefix='EnvSampler-')
else:
logger.log(
"Obtaining samples from the environment using the policy..."
)
env_paths = self.env_sampler.obtain_samples(
log=True, log_prefix='EnvSampler-')
self.policy.obs_filter.stats_increment()
logger.record_tabular('Time-EnvSampling',
time.time() - time_env_sampling_start)
logger.log("Processing environment samples...")
# first processing just for logging purposes
time_env_samp_proc = time.time()
samples_data = self.dynamics_sample_processor.process_samples(
env_paths, log=True, log_prefix='EnvTrajs-')
self.env.log_diagnostics(env_paths, prefix='EnvTrajs-')
logger.record_tabular('Time-EnvSampleProc',
time.time() - time_env_samp_proc)
''' --------------- fit dynamics model --------------- '''
time_fit_start = time.time()
logger.log("Training dynamics model for %i epochs ..." %
self.dynamics_model_max_epochs)
if self.dynamics_model is not None:
self.dynamics_model.fit(
samples_data['observations'],
samples_data['actions'],
samples_data['next_observations'],
epochs=self.dynamics_model_max_epochs,
verbose=False,
log_tabular=True,
compute_normalization=True)
buffer = None if not self.sample_from_buffer else samples_data
logger.record_tabular('Time-ModelFit',
time.time() - time_fit_start)
# returns = np.mean(samples_data['returns'])
# if returns < self._last_returns:
# self.policy.set_params(self._prev_policy)
# self._last_returns = returns
# self._prev_policy = self.policy.get_params()
''' ------------ log real performance --------------- '''
# if self.log_real_performance:
# logger.log("Evaluating the performance of the real policy")
# env_paths = self.env_sampler.obtain_samples(log=True, log_prefix='RealPolicy-')
# _ = self.model_sample_processor.process_samples(env_paths, log='all', log_prefix='PrePolicy-')
''' --------------- RS steps --------------- '''
times_dyn_sampling = []
times_dyn_sample_processing = []
times_itr = []
times_rs_steps = []
list_sampling_time = []
list_proc_samples_time = []
for rs_itr in range(steps_per_iter[itr]):
time_itr_start = time.time()
logger.log("\n -------------- RS-Step %d --------------" %
int(sum(steps_per_iter[:itr]) + rs_itr))
deltas = self.policy.get_deltas(self.num_deltas)
self.policy.set_deltas(deltas,
delta_std=self.delta_std,
symmetrical=True)
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
samples_data = self.model_sampler.obtain_samples(
log=True, log_prefix='Models-', buffer=buffer)
list_sampling_time.append(time.time() -
time_env_sampling_start)
""" ---------------------- Processing --------------------- """
# TODO: Add preprocessing of the state to see what sort of update rule between the models we want
samples_data = self.ars_sample_processor.process_samples(
samples_data, log=True, log_prefix='step%d-' % rs_itr)
if self.dynamics_model is None:
self.policy.stats_increment()
""" ------------------ Outer Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
time_rs_start = time.time()
self.algo.optimize_policy(samples_data['returns'], deltas)
times_dyn_sampling.append(list_sampling_time)
times_dyn_sample_processing.append(list_proc_samples_time)
times_rs_steps.append(time.time() - time_rs_start)
times_itr.append(time.time() - time_itr_start)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
if self.dynamics_model is None:
logger.logkv('n_timesteps',
self.model_sampler.total_timesteps_sampled)
else:
logger.logkv('n_timesteps',
self.env_sampler.total_timesteps_sampled)
logger.logkv('AvgTime-RS', np.mean(times_rs_steps))
logger.logkv('AvgTime-SampleProc',
np.mean(times_dyn_sample_processing))
logger.logkv('AvgTime-Sampling', np.mean(times_dyn_sampling))
logger.logkv('AvgTime-ModelItr', np.mean(times_itr))
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
start_time = time.time()
if self.start_itr == 0:
self.algo._update_target(tau=1.0)
if self.n_initial_exploration_steps > 0:
while self.replay_buffer._size < self.n_initial_exploration_steps:
paths = self.sampler.obtain_samples(
log=True, log_prefix='train-', random=True)
samples_data = self.sample_processor.process_samples(
paths, log='all', log_prefix='train-')
sample_num = samples_data['observations'].shape[0]
for i in range(sample_num):
self.replay_buffer.add_sample(
samples_data['observations'][i],
samples_data['actions'][i],
samples_data['rewards'][i],
samples_data['dones'][i],
samples_data['next_observations'][i],
)
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
logger.log(
"Sampling set of tasks/goals for this meta-batch...")
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
paths = self.sampler.obtain_samples(log=True,
log_prefix='train-')
sampling_time = time.time() - time_env_sampling_start
""" ----------------- Processing Samples ---------------------"""
# check how the samples are processed
logger.log("Processing samples...")
time_proc_samples_start = time.time()
samples_data = self.sample_processor.process_samples(
paths, log='all', log_prefix='train-')
sample_num = samples_data['observations'].shape[0]
for i in range(sample_num):
self.replay_buffer.add_sample(
samples_data['observations'][i],
samples_data['actions'][i], samples_data['rewards'][i],
samples_data['dones'][i],
samples_data['next_observations'][i])
proc_samples_time = time.time() - time_proc_samples_start
paths = self.sampler.obtain_samples(log=True,
log_prefix='eval-',
deterministic=True)
_ = self.sample_processor.process_samples(paths,
log='all',
log_prefix='eval-')
self.log_diagnostics(paths, prefix='train-')
""" ------------------ Policy Update ---------------------"""
logger.log("Optimizing policy...")
time_optimization_step_start = time.time()
self.algo.optimize_policy(self.replay_buffer,
itr - self.start_itr,
self.num_grad_steps)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps',
self.sampler.total_timesteps_sampled)
logger.logkv('Time-Optimization',
time.time() - time_optimization_step_start)
logger.logkv('Time-SampleProc', np.sum(proc_samples_time))
logger.logkv('Time-Sampling', sampling_time)
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.log("Training finished")
self.sess.close()