def optimize_policy(self, all_samples_data, log=True):
"""
Performs MAML outer step
Args:
all_samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
meta_op_input_dict = self._extract_input_dict_meta_op(
all_samples_data, self._optimization_keys)
logger.log("Computing KL before")
mean_kl_before = self.optimizer.constraint_val(meta_op_input_dict)
logger.log("Computing loss before")
loss_before = self.optimizer.loss(meta_op_input_dict)
logger.log("Optimizing")
self.optimizer.optimize(meta_op_input_dict)
logger.log("Computing loss after")
loss_after = self.optimizer.loss(meta_op_input_dict)
logger.log("Computing KL after")
mean_kl = self.optimizer.constraint_val(meta_op_input_dict)
if log:
logger.logkv('MeanKLBefore', mean_kl_before)
logger.logkv('MeanKL', mean_kl)
logger.logkv('LossBefore', loss_before)
logger.logkv('LossAfter', loss_after)
logger.logkv('dLoss', loss_before - loss_after)
def optimize(self, input_val_dict):
"""
Carries out the optimization step
Args:
input_val_dict (dict): dict containing the values to be fed into the computation graph
Returns:
(float) loss before optimization
"""
sess = tf.get_default_session()
feed_dict = self.create_feed_dict(input_val_dict)
# Overload self._batch size
# dataset = MAMLBatchDataset(inputs, num_batches=self._batch_size, extra_inputs=extra_inputs, meta_batch_size=self.meta_batch_size, num_grad_updates=self.num_grad_updates)
# Todo: reimplement minibatches
loss_before_opt = None
for epoch in range(self._max_epochs):
if self._verbose:
logger.log("Epoch %d" % epoch)
loss, _ = sess.run([self._loss, self._train_op], feed_dict)
if not loss_before_opt: loss_before_opt = loss
# if self._verbose:
# logger.log("Epoch: %d | Loss: %f" % (epoch, new_loss))
#
# if abs(last_loss - new_loss) < self._tolerance:
# break
# last_loss = new_loss
return loss_before_opt
def optimize(self, input_val_dict):
"""
Carries out the optimization step
Args:
input_val_dict (dict): dict containing the values to be fed into the computation graph
Returns:
(float) loss before optimization
"""
sess = tf.get_default_session()
feed_dict = self.create_feed_dict(input_val_dict)
loss_before_opt = None
for epoch in range(self._max_epochs):
if self._verbose:
logger.log("Epoch %d" % epoch)
loss, _ = sess.run([self._loss, self._train_op], feed_dict)
if not loss_before_opt:
loss_before_opt = loss
return loss_before_opt
def optimize_policy(self, samples_data, log=True):
"""
Performs MAML outer step
Args:
all_samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
input_dict = self._extract_input_dict(samples_data,
self._optimization_keys,
prefix='train')
if log: logger.log("Optimizing")
loss_before = self.optimizer.optimize(input_val_dict=input_dict)
if log: logger.log("Computing statistics")
loss_after = self.optimizer.loss(input_val_dict=input_dict)
if log:
logger.logkv('LossBefore', loss_before)
logger.logkv('LossAfter', loss_after)
def compute_gradients(self, all_samples_data, log=True):
meta_op_input_dict = self._extract_input_dict_meta_op(
all_samples_data, self._optimization_keys)
feed_dict = utils.create_feed_dict(
placeholder_dict=self.meta_op_phs_dict,
value_dict=meta_op_input_dict)
if log: logger.log("compute gradients")
gradients_values = tf.get_default_session().run(self.gradients,
feed_dict=feed_dict)
return gradients_values
def train(self):
for i in range(1, self.eff+1):
with self.sess.as_default() as sess:
logger.log("----------- Adaptation rollouts per meta-task = ", i, " -----------")
# self.sampler.rollouts_per_meta_task = 10000
self.sampler.update_batch_size(i)
# 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))
self.task = self.env.sample_tasks(self.sampler.meta_batch_size, is_eval=True)
self.sampler.set_tasks(self.task)
#logger.log("\n ---------------- Iteration %d ----------------" % itr)
logger.log("Sampling set of tasks/goals for this meta-batch...")
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
paths = self.sampler.obtain_samples(log=True, log_prefix='train-')
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples...")
samples_data = self.sample_processor.process_samples(paths, log='all', log_prefix='train-')
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('n_timesteps', self.sampler.total_timesteps_sampled)
#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 optimize(self, input_val_dict):
"""
Carries out the optimization step
Args:
input_val_dict (dict): dict containing the values to be fed into the computation graph
Returns:
(float) loss before optimization
"""
sess = tf.get_default_session()
batch_size, seq_len, *_ = list(input_val_dict.values())[0].shape
loss_before_opt = None
for epoch in range(self._max_epochs):
hidden_batch = self._target.get_zero_state(batch_size)
if self._verbose:
logger.log("Epoch %d" % epoch)
# run train op
loss = []
all_grads = []
for i in range(0, seq_len, self._backprop_steps):
n_i = i + self._backprop_steps
feed_dict = dict([(self._input_ph_dict[key],
input_val_dict[key][:, i:n_i])
for key in self._input_ph_dict.keys()])
feed_dict[self._hidden_ph] = hidden_batch
batch_loss, grads, hidden_batch = sess.run(
[self._loss, self._gradients_var, self._next_hidden_var],
feed_dict=feed_dict)
loss.append(batch_loss)
all_grads.append(grads)
grads = [np.mean(grad, axis=0) for grad in zip(*all_grads)]
feed_dict = dict(zip(self._gradients_ph, grads))
_ = sess.run(self._train_op, feed_dict=feed_dict)
if not loss_before_opt: loss_before_opt = np.mean(loss)
# if self._verbose:
# logger.log("Epoch: %d | Loss: %f" % (epoch, new_loss))
#
# if abs(last_loss - new_loss) < self._tolerance:
# break
# last_loss = new_loss
return loss_before_opt
def optimize_policy(self, all_samples_data, log=True):
"""
Performs MAML outer step
Args:
all_samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
meta_op_input_dict = self._extract_input_dict_meta_op(all_samples_data, self._optimization_keys)
# add kl_coeffs / clip_eps to meta_op_input_dict
meta_op_input_dict['inner_kl_coeff'] = self.inner_kl_coeff
meta_op_input_dict['clip_eps'] = self.clip_eps
if log: logger.log("Optimizing")
loss_before = self.optimizer.optimize(input_val_dict=meta_op_input_dict)
if log: logger.log("Computing statistics")
loss_after, inner_kls, outer_kl = self.optimizer.compute_stats(input_val_dict=meta_op_input_dict)
if self.adaptive_inner_kl_penalty:
if log: logger.log("Updating inner KL loss coefficients")
self.inner_kl_coeff = self.adapt_kl_coeff(self.inner_kl_coeff, inner_kls, self.target_inner_step)
if log:
logger.logkv('LossBefore', loss_before)
logger.logkv('LossAfter', loss_after)
logger.logkv('KLInner', np.mean(inner_kls))
logger.logkv('KLCoeffInner', np.mean(self.inner_kl_coeff))
def optimize_policy(self,
all_samples_data,
mod_samples_data,
num_paths_per_rollout,
log=True):
"""
Performs MAML outer step
Args:
all_samples_data (list) : list of lists of lists of samples (each is a dict) split by gradient update and
meta task
log (bool) : whether to log statistics
Returns:
None
"""
meta_op_input_dict = self._extract_input_dict_meta_op(
all_samples_data, self._optimization_keys)
extra_feed_dict = {
self.policy.mod_input_var: mod_samples_data,
self.policy.num_paths_var: num_paths_per_rollout,
}
# add kl_coeffs / clip_eps to meta_op_input_dict
meta_op_input_dict['inner_kl_coeff'] = self.inner_kl_coeff
meta_op_input_dict['clip_eps'] = self.clip_eps
if log: logger.log("Optimizing")
loss_before, grad_norms = self.optimizer.optimize(
input_val_dict=meta_op_input_dict, extra_feed_dict=extra_feed_dict)
if self.summary_writer is not None:
for name, norm in grad_norms.items():
tensorboard_util.log_scalar(self.summary_writer,
'grads/' + name, norm,
self.log_step)
self.log_step += 1
if log: logger.log("Computing statistics")
loss_after, inner_kls, outer_kl = self.optimizer.compute_stats(
input_val_dict=meta_op_input_dict, extra_feed_dict=extra_feed_dict)
if self.adaptive_inner_kl_penalty:
if log: logger.log("Updating inner KL loss coefficients")
self.inner_kl_coeff = self.adapt_kl_coeff(self.inner_kl_coeff,
inner_kls,
self.target_inner_step)
if log:
logger.logkv('LossBefore', loss_before)
logger.logkv('LossAfter', loss_after)
logger.logkv('KLInner', np.mean(inner_kls))
logger.logkv('KLCoeffInner', np.mean(self.inner_kl_coeff))
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/meta_policy_search_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)
#writer.add_scalar(self.algo.name, self.sample_processor.AR, 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()
logger.log("Training finished")
self.sess.close()
def train(self):
policy_0 = self.policy
for i in [4, 3, 2, 1]: #range(1, self.eff+1):
print("On", i, "self.policy == policy_0: ",
self.policy == policy_0)
with self.sess.as_default() as sess:
logger.log("----------- Adaptation rollouts per meta-task = ",
i, " -----------")
undiscounted_returns = []
for j in range(0, self.env.NUM_EVAL,
self.sampler.meta_batch_size):
logger.log("---------Testing on task", j, "~",
j + self.sampler.meta_batch_size - 1,
"---------")
# 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))
uninit_vars = [var for var in tf.global_variables()]
sess.run(tf.variables_initializer(uninit_vars))
logger.log(
"Sampling set of tasks/goals for this meta-batch...")
self.sampler.update_tasks(
test=True, start_from=j) # sample from test split!
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
for step in range(self.num_inner_grad_steps + 1):
if step < self.num_inner_grad_steps:
self.sampler.update_batch_size_v2(
i) ######################
logger.log("On step-0: Obtaining samples...")
else:
self.sampler.update_batch_size(2)
logger.log("On step-1: Obtaining samples...")
paths = self.sampler.obtain_samples(
log=False,
test=True) # log_prefix='test-Step_%d-' % step
logger.log("On Test: Processing Samples...")
samples_data = self.sample_processor.process_samples(
paths, log=False
) # log='all', log_prefix='test-Step_%d-' % step
self.log_diagnostics(sum(list(paths.values()), []),
prefix='test-Step_%d-' % step)
""" ------------------- Inner Policy Update / logging returns --------------------"""
if step < self.num_inner_grad_steps:
logger.log(
"On Test: Computing inner policy updates...")
self.algo._adapt(samples_data)
else:
paths = self.sample_processor.gao_paths(paths)
undiscounted_returns.extend(
[sum(path["rewards"]) for path in paths])
test_average_return = np.mean(undiscounted_returns)
logger.logkv('x', i)
logger.logkv('return', test_average_return)
logger.dumpkvs()
logger.log("------Testing rollouts per meta-task = ", i,
"finished------")
'''
def main(config):
set_seed(config['seed'])
baseline = globals()[config['baseline']]() #instantiate baseline
env = globals()[config['env']]() # instantiate env
env = normalize(env) # apply normalize wrapper to env
policy = MetaGaussianMLPPolicy(
name="meta-policy",
obs_dim=np.prod(env.observation_space.shape),
action_dim=np.prod(env.action_space.shape),
meta_batch_size=config['meta_batch_size'],
hidden_sizes=config['hidden_sizes'],
)
sampler = MetaSampler(
env=env,
policy=policy,
rollouts_per_meta_task=config[
'rollouts_per_meta_task'], # This batch_size is confusing
meta_batch_size=config['meta_batch_size'],
max_path_length=config['max_path_length'],
parallel=config['parallel'],
)
sample_processor = MetaSampleProcessor(
baseline=baseline,
discount=config['discount'],
gae_lambda=config['gae_lambda'],
normalize_adv=config['normalize_adv'],
)
algo = ProMP(
policy=policy,
inner_lr=config['inner_lr'],
meta_batch_size=config['meta_batch_size'],
num_inner_grad_steps=config['num_inner_grad_steps'],
learning_rate=config['learning_rate'],
num_ppo_steps=config['num_promp_steps'],
clip_eps=config['clip_eps'],
target_inner_step=config['target_inner_step'],
init_inner_kl_penalty=config['init_inner_kl_penalty'],
adaptive_inner_kl_penalty=config['adaptive_inner_kl_penalty'],
)
gpu_config = tf.ConfigProto()
gpu_config.gpu_options.allow_growth = True # pylint: disable=E1101
sess = tf.Session(config=gpu_config)
saver = tf.train.Saver(
keep_checkpoint_every_n_hours=config['keep_checkpoint_every_n_hours'],
max_to_keep=config['max_checkpoints_to_keep'])
save_path = os.path.join(args.dump_path, 'model.ckpt')
if config['restore_path'] is not None:
logger.log('Restoring parameters from {}'.format(
config['restore_path']))
saver.restore(sess, config['restore_path'])
logger.log('Restored')
trainer = Trainer(
algo=algo,
policy=policy,
env=env,
sampler=sampler,
sample_processor=sample_processor,
saver=saver,
save_path=save_path,
save_steps=config['save_steps'],
n_itr=config['n_itr'],
num_inner_grad_steps=config['num_inner_grad_steps'],
sess=sess,
)
trainer.train()
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() # sample 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()
'''
if step == self.num_inner_grad_steps:
temp = self.sampler.batch_size
self.sampler.update_batch_size(2)
paths = self.sampler.obtain_samples(log=True, log_prefix='Step_%d-' % step)
self.sampler.update_batch_size(temp)
else:
paths = self.sampler.obtain_samples(log=True, log_prefix='Step_%d-' % step)
'''
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/meta_policy_search_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)
""" ------------------ Test-split Performance for logging ---------------------"""
logger.log(
"Testing on test-tasks split for logging, rollout_per_task = 20..."
)
undiscounted_returns = []
for i in range(0, self.env.NUM_EVAL,
self.sampler.meta_batch_size):
self.sampler.update_tasks(
test=True, start_from=i) # sample from test split!
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
for step in range(self.num_inner_grad_steps + 1):
logger.log("On Test: Obtaining samples...")
paths = self.sampler.obtain_samples(
log=False,
test=True) # log_prefix='test-Step_%d-' % step
logger.log("On Test: Processing Samples...")
samples_data = self.sample_processor.process_samples(
paths, log=False
) # log='all', log_prefix='test-Step_%d-' % step
self.log_diagnostics(sum(list(paths.values()), []),
prefix='test20-Step_%d-' % step)
""" ------------------- Inner Policy Update / logging returns --------------------"""
if step < self.num_inner_grad_steps:
logger.log(
"On Test: Computing inner policy updates...")
self.algo._adapt(samples_data)
else:
paths = self.sample_processor.gao_paths(paths)
undiscounted_returns.extend(
[sum(path["rewards"]) for path in paths])
test_average_return = np.mean(undiscounted_returns)
logger.logkv('test20-AverageReturn', test_average_return)
logger.log(
"Testing on test-tasks split for logging, rollout_per_task = 2..."
)
sampler_batch_size = self.sampler.batch_size
self.sampler.update_batch_size(2) ##############
undiscounted_returns = []
for i in range(0, self.env.NUM_EVAL,
self.sampler.meta_batch_size):
self.sampler.update_tasks(
test=True, start_from=i) # sample from test split!
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
for step in range(self.num_inner_grad_steps + 1):
logger.log("On Test: Obtaining samples...")
paths = self.sampler.obtain_samples(
log=False,
test=True) # log_prefix='test-Step_%d-' % step
logger.log("On Test: Processing Samples...")
samples_data = self.sample_processor.process_samples(
paths, log=False
) # log='all', log_prefix='test-Step_%d-' % step
self.log_diagnostics(sum(list(paths.values()), []),
prefix='test-Step_%d-' % step)
""" ------------------- Inner Policy Update / logging returns --------------------"""
if step < self.num_inner_grad_steps:
logger.log(
"On Test: Computing inner policy updates...")
self.algo._adapt(samples_data)
else:
paths = self.sample_processor.gao_paths(paths)
undiscounted_returns.extend(
[sum(path["rewards"]) for path in paths])
test_average_return = np.mean(undiscounted_returns)
self.sampler.update_batch_size(sampler_batch_size)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps',
self.sampler.total_timesteps_sampled)
logger.logkv('test-AverageReturn', test_average_return)
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()
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))
n_timesteps = 0
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)
gradients = []
for i in range(self.num_sapling_rounds):
logger.log("\n ----- Sampling Round %d ---" % i)
dry = i < self.num_sapling_rounds - 1
if not dry: self.sampler.update_tasks()
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
all_samples_data, all_paths = [], []
for step in range(self.num_inner_grad_steps + 1):
logger.log('** Step ' + str(step) + ' **')
logger.log("Obtaining samples...")
paths = self.sampler.obtain_samples(
log=True, log_prefix='Step_%d-' % step)
all_paths.append(paths)
logger.log("Processing samples...")
samples_data = self.sample_processor.process_samples(
paths, log='all', log_prefix='Step_%d-' % step)
all_samples_data.append(samples_data)
if not dry:
self.log_diagnostics(sum(list(paths.values()), []),
prefix='Step_%d-' % step)
if step < self.num_inner_grad_steps:
logger.log("Computing inner policy updates...")
self.algo._adapt(samples_data)
""" compute gradients """
gradients.append(
self.algo.compute_gradients(all_samples_data))
if not dry:
""" ------------ Compute and log gradient variance ------------"""
# compute variance of adaptation gradients
for step_id in range(self.num_inner_grad_steps):
meta_batch_size = len(gradients[0][0])
grad_std, grad_rstd = [], []
for task_id in range(meta_batch_size):
stacked_grads = np.stack([
gradients[round_id][step_id][task_id] for
round_id in range(self.num_sapling_rounds)
],
axis=1)
std = np.std(stacked_grads, axis=1)
mean = np.abs(np.mean(stacked_grads, axis=1))
grad_std.append(np.mean(std))
grad_rstd.append(np.mean(std / mean))
logger.logkv('Step_%i-GradientMean', np.mean(mean))
logger.logkv('Step_%i-GradientStd' % step_id,
np.mean(grad_std))
logger.logkv('Step_%i-GradientRStd' % step_id,
np.mean(grad_rstd))
# compute variance of meta gradients
stacked_grads = np.stack([
gradients[round_id][self.num_inner_grad_steps]
for round_id in range(self.num_sapling_rounds)
],
axis=1)
std = np.std(stacked_grads, axis=1)
mean = np.abs(np.mean(stacked_grads, axis=1))
meta_grad_std = np.mean(std)
meta_grad_rstd = np.mean(std / (mean + 1e-8))
meta_grad_rvar = np.mean(std**2 / (mean + 1e-8))
logger.logkv('Meta-GradientMean', np.mean(mean))
logger.logkv('Meta-GradientStd', meta_grad_std)
logger.logkv('Meta-GradientRStd', meta_grad_rstd)
logger.logkv('Meta-GradientRVariance', meta_grad_rvar)
# compute cosine dists
cosine_dists = cdist(np.transpose(stacked_grads),
np.transpose(
np.mean(stacked_grads,
axis=1).reshape(
(-1, 1))),
metric='cosine')
mean_abs_cos_dist = np.mean(np.abs(cosine_dists))
mean_squared_cosine_dists = np.mean(cosine_dists**2)
mean_squared_cosine_dists_sqrt = np.sqrt(
mean_squared_cosine_dists)
logger.logkv('Meta-GradientCosAbs', mean_abs_cos_dist)
logger.logkv('Meta-GradientCosVar',
mean_squared_cosine_dists)
logger.logkv('Meta-GradientCosStd',
mean_squared_cosine_dists_sqrt)
""" ------------------ Outer Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
self.algo.optimize_policy(all_samples_data)
""" ------------------- Logging Stuff --------------------------"""
n_timesteps += (self.num_inner_grad_steps +
1) * self.sampler.total_samples
logger.logkv('n_timesteps', n_timesteps)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr) # , **kwargs)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.logkv('Itr', itr)
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.dumpkvs()
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.task = self.env.sample_tasks(self.sampler.meta_batch_size)
self.sampler.set_tasks(self.task)
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
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)
""" ------------------ Test-split Performance for logging ---------------------"""
logger.log("Testing on test-tasks split for logging...")
sampler_batch_size = self.sampler.batch_size
self.sampler.update_batch_size(3) ####################2
undiscounted_returns = []
for i in range(0, self.env.NUM_EVAL,
self.sampler.meta_batch_size):
# Caution: Here actually i in [0] since self.meta_batch_size=100(when running on linux)
self.sampler.update_tasks(
test=True, start_from=i) # sample from test split!
#self.policy.switch_to_pre_update() # Switch to pre-update policy
logger.log("On Test: Obtaining samples...")
paths = self.sampler.obtain_samples(
log=False,
test=True) # log_prefix='test-Step_%d-' % step
logger.log("On Test: Processing Samples...")
self.log_diagnostics(sum(list(paths.values()), []),
prefix='test-')
""" ------------------- Logging Returns --------------------"""
paths = self.sample_processor.gao_paths(paths)
undiscounted_returns.extend(
[sum(path["rewards"]) for path in paths])
test_average_return = np.mean(undiscounted_returns)
self.sampler.update_batch_size(sampler_batch_size)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps',
self.sampler.total_timesteps_sampled)
logger.logkv('test-AverageReturn', test_average_return)
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 optimize(self, input_val_dict):
"""
Carries out the optimization step
Args:
inputs (list): inputs for the optimization
extra_inputs (list): extra inputs for the optimization
subsample_grouped_inputs (None or list): subsample data from each element of the list
"""
logger.log("Start CG optimization")
logger.log("computing loss before")
loss_before = self.loss(input_val_dict)
logger.log("performing update")
logger.log("computing gradient")
gradient = self.gradient(input_val_dict)
logger.log("gradient computed")
logger.log("computing descent direction")
Hx = self._hvp_approach.build_eval(input_val_dict)
descent_direction = conjugate_gradients(Hx,
gradient,
cg_iters=self._cg_iters)
initial_step_size = np.sqrt(
2.0 * self._max_constraint_val *
(1. / (descent_direction.dot(Hx(descent_direction)) + 1e-8)))
if np.isnan(initial_step_size):
logger.log("Initial step size is NaN! Rejecting the step!")
return
initial_descent_step = initial_step_size * descent_direction
logger.log("descent direction computed")
prev_params = self._target.get_param_values()
prev_params_values = _flatten_params(prev_params)
loss, constraint_val, n_iter, violated = 0, 0, 0, False
for n_iter, ratio in enumerate(self._backtrack_ratio**np.arange(
self._max_backtracks)):
cur_step = ratio * initial_descent_step
cur_params_values = prev_params_values - cur_step
cur_params = _unflatten_params(cur_params_values,
params_example=prev_params)
self._target.set_params(cur_params)
loss, constraint_val = self.loss(
input_val_dict), self.constraint_val(input_val_dict)
if loss < loss_before and constraint_val <= self._max_constraint_val:
break
""" ------------------- Logging Stuff -------------------------- """
if np.isnan(loss):
violated = True
logger.log("Line search violated because loss is NaN")
if np.isnan(constraint_val):
violated = True
logger.log("Line search violated because constraint %s is NaN" %
self._constraint_name)
if loss >= loss_before:
violated = True
logger.log("Line search violated because loss not improving")
if constraint_val >= self._max_constraint_val:
violated = True
logger.log(
"Line search violated because constraint %s is violated" %
self._constraint_name)
if violated and not self._accept_violation:
logger.log("Line search condition violated. Rejecting the step!")
self._target.set_params(prev_params)
logger.log("backtrack iters: %d" % n_iter)
logger.log("computing loss after")
logger.log("optimization finished")
policy=policy,
inner_lr=params['inner_lr'],
meta_batch_size=params['meta_batch_size'],
num_inner_grad_steps=params['num_inner_grad_steps'],
learning_rate=params['learning_rate'],
num_ppo_steps=params['num_promp_steps'],
clip_eps=params['clip_eps'],
target_inner_step=params['target_inner_step'],
init_inner_kl_penalty=params['init_inner_kl_penalty'],
adaptive_inner_kl_penalty=params['adaptive_inner_kl_penalty'],
)
saver = tf.train.Saver()
if args.restore_path is not None:
logger.log('Restoring parameters from {}'.format(args.restore_path))
saver.restore(sess, args.restore_path)
logger.log('Restored')
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))
wrapped_env = env
while hasattr(wrapped_env, '_wrapped_env'):
wrapped_env = wrapped_env._wrapped_env
frame_skip = wrapped_env.frame_skip if hasattr(wrapped_env,
'frame_skip') else 1
