def log_diagnostics(self, paths, prefix=''):
progs = [
path["observations"][-1][-3] - path["observations"][0][-3]
for path in paths
]
logger.record_tabular(prefix+'AverageForwardProgress', np.mean(progs))
logger.record_tabular(prefix+'MaxForwardProgress', np.max(progs))
logger.record_tabular(prefix+'MinForwardProgress', np.min(progs))
logger.record_tabular(prefix+'StdForwardProgress', np.std(progs))
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))
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))
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())
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))
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()