def train(self):
"""
Trains policy on env using algo
"""
time_total = time.time()
''' --------------- worker looping --------------- '''
futures = [worker.start.remote() for worker in self.workers]
logger.log('Start looping...')
ray.get(futures)
logger.logkv('Trainer-TimeTotal', time.time() - time_total)
logger.dumpkvs()
logger.log('***** Training finished ******')
def prepare_start(self, env_pickle, policy_pickle, baseline_pickle,
dynamics_model_pickle, feed_dict, algo_str, config):
import tensorflow as tf
self.sess = sess = tf.Session(config=config)
with sess.as_default():
""" --------------------- Construct instances -------------------"""
from asynch_mb.samplers.bptt_samplers.bptt_sampler import BPTTSampler
from asynch_mb.samplers.base import SampleProcessor
from asynch_mb.algos.ppo import PPO
from asynch_mb.algos.trpo import TRPO
env = pickle.loads(env_pickle)
policy = pickle.loads(policy_pickle)
baseline = pickle.loads(baseline_pickle)
dynamics_model = pickle.loads(dynamics_model_pickle)
sess.run(tf.initializers.global_variables())
self.policy = policy
self.baseline = baseline
self.model_sampler = BPTTSampler(env=env,
policy=policy,
dynamics_model=dynamics_model,
**feed_dict['model_sampler'])
self.model_sample_processor = SampleProcessor(
baseline=baseline, **feed_dict['model_sample_processor'])
if algo_str == 'meppo':
self.algo = PPO(policy=policy, **feed_dict['algo'])
elif algo_str == 'metrpo':
self.algo = TRPO(policy=policy, **feed_dict['algo'])
else:
raise NotImplementedError(f'got algo_str {algo_str}')
""" -------------------- Pull pickled model from model parameter server ---------------- """
dynamics_model = pickle.loads(dynamics_model_pickle)
self.model_sampler.dynamics_model = dynamics_model
if hasattr(self.model_sampler, 'vec_env'):
self.model_sampler.vec_env.dynamics_model = dynamics_model
""" -------------------- Step and Push ------------------- """
self.step()
self.push()
logger.dumpkvs()
return 1
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 start(self):
logger.log(f"\n================ {self.name} starts ===============")
time_start = time.time()
with self.sess.as_default():
# loop
while not ray.get(self.stop_cond.is_set.remote()):
do_synch, do_step = self.step_wrapper()
self.synch_counter += do_synch
self.step_counter += do_step
# logging
logger.logkv(self.name + '-TimeSoFar',
time.time() - time_start)
logger.logkv(self.name + '-TotalStep', self.step_counter)
logger.logkv(self.name + '-TotalSynch', self.synch_counter)
logger.dumpkvs()
self.set_stop_cond()
logger.log(
f"\n================== {self.name} closed ===================")
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)
''' --------------- 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))
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 __call__(
self,
exp_dir,
policy_pickle,
env_pickle,
baseline_pickle,
dynamics_model_pickle,
feed_dict,
queue_prev,
queue,
queue_next,
remote,
start_itr,
n_itr,
stop_cond,
need_query,
auto_push,
config,
):
time_start = time.time()
self.name = current_process().name
logger.configure(dir=exp_dir + '/' + self.name,
format_strs=['csv', 'stdout', 'log'],
snapshot_mode=self.snapshot_mode,
snapshot_gap=self.snapshot_gap)
self.n_itr = n_itr
self.queue_prev = queue_prev
self.queue = queue
self.queue_next = queue_next
self.stop_cond = stop_cond
# FIXME: specify CPU/GPU usage here
import tensorflow as tf
def _init_vars():
sess = tf.get_default_session()
sess.run(tf.initializers.global_variables())
with tf.Session(config=config).as_default():
self.construct_from_feed_dict(
policy_pickle,
env_pickle,
baseline_pickle,
dynamics_model_pickle,
feed_dict,
)
_init_vars()
# warm up
self.itr_counter = start_itr
if self.verbose:
print('{} waiting for starting msg from trainer...'.format(
self.name))
assert remote.recv() == 'prepare start'
self.prepare_start()
remote.send('loop ready')
logger.dumpkvs()
logger.log("\n============== {} is ready =============".format(
self.name))
assert remote.recv() == 'start loop'
total_push, total_synch, total_step = 0, 0, 0
while not self.stop_cond.is_set():
if self.verbose:
logger.log(
"\n------------------------- {} starting new loop ------------------"
.format(self.name))
if need_query: # poll
time_poll = time.time()
queue_prev.put('push')
time_poll = time.time() - time_poll
logger.logkv('{}-TimePoll'.format(self.name), time_poll)
do_push, do_synch, do_step = self.process_queue()
# step
if do_step:
self.itr_counter += 1
self.step()
if auto_push:
do_push += 1
self.push()
# Assuming doing autopush for all
assert do_push == 1
assert do_step == 1
total_push += do_push
total_synch += do_synch
total_step += do_step
logger.logkv(self.name + '-TimeSoFar',
time.time() - time_start)
logger.logkv(self.name + '-TotalPush', total_push)
logger.logkv(self.name + '-TotalSynch', total_synch)
logger.logkv(self.name + '-TotalStep', total_step)
if total_synch > 0:
logger.logkv(self.name + '-StepPerSynch',
total_step / total_synch)
logger.dumpkvs()
logger.log(
"\n========================== {} {}, total {} ==================="
.format(
self.name,
(do_push, do_synch, do_step),
(total_push, total_synch, total_step),
))
self.set_stop_cond()
remote.send('loop done')
logger.log("\n================== {} closed ===================".format(
self.name))
remote.send('worker closed')
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)
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.sampler.obtain_samples(log=True,
random=True,
log_prefix='')
elif self.initial_sinusoid_samples and itr == 0:
logger.log(
"Obtaining sinusoidal samples from the environment using the policy..."
)
env_paths = self.sampler.obtain_samples(log=True,
log_prefix='',
sinusoid=True)
else:
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.dynamics_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)
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()