def _train(self, env, policy, pool):
"""Perform RL training.
Args:
env (`rllab.Env`): Environment used for training
policy (`Policy`): Policy used for training
pool (`PoolBase`): Sample pool to add samples to
"""
self._init_training()
self.sampler.initialize(env, policy, pool)
evaluation_env = deep_clone(env) if self._eval_n_episodes else None
with tf_utils.get_default_session().as_default():
gt.rename_root('RLAlgorithm')
gt.reset()
gt.set_def_unique(False)
for epoch in gt.timed_for(range(self._n_epochs + 1),
save_itrs=True):
logger.push_prefix('Epoch #%d | ' % epoch)
for t in range(self._epoch_length):
self.sampler.sample()
if not self.sampler.batch_ready():
continue
gt.stamp('sample')
for i in range(self._n_train_repeat):
self._do_training(iteration=t +
epoch * self._epoch_length,
batch=self.sampler.random_batch())
gt.stamp('train')
self._evaluate(policy, evaluation_env)
gt.stamp('eval')
params = self.get_snapshot(epoch)
logger.save_itr_params(epoch, params)
time_itrs = gt.get_times().stamps.itrs
time_eval = time_itrs['eval'][-1]
time_total = gt.get_times().total
time_train = time_itrs.get('train', [0])[-1]
time_sample = time_itrs.get('sample', [0])[-1]
logger.record_tabular('time-train', time_train)
logger.record_tabular('time-eval', time_eval)
logger.record_tabular('time-sample', time_sample)
logger.record_tabular('time-total', time_total)
logger.record_tabular('epoch', epoch)
self.sampler.log_diagnostics()
logger.dump_tabular(with_prefix=False)
logger.pop_prefix()
self.sampler.terminate()
def _train(self, env, policy, pool):
"""Perform RL training.
Args:
env (`rllab.Env`): Environment used for training
policy (`Policy`): Policy used for training
pool (`PoolBase`): Sample pool to add samples to
"""
self._init_training()
self.sampler.initialize(env, policy, pool)
evaluation_env = deep_clone(env) if self._eval_n_episodes else None
with tf_utils.get_default_session().as_default():
gt.rename_root('RLAlgorithm')
gt.reset()
gt.set_def_unique(False)
for epoch in gt.timed_for(
range(self._n_epochs + 1), save_itrs=True):
logger.push_prefix('Epoch #%d | ' % epoch)
for t in range(self._epoch_length):
self.sampler.sample()
if not self.sampler.batch_ready():
continue
gt.stamp('sample')
for i in range(self._n_train_repeat):
self._do_training(
iteration=t + epoch * self._epoch_length,
batch=self.sampler.random_batch())
gt.stamp('train')
self._evaluate(policy, evaluation_env)
gt.stamp('eval')
params = self.get_snapshot(epoch)
logger.save_itr_params(epoch, params)
time_itrs = gt.get_times().stamps.itrs
time_eval = time_itrs['eval'][-1]
time_total = gt.get_times().total
time_train = time_itrs.get('train', [0])[-1]
time_sample = time_itrs.get('sample', [0])[-1]
logger.record_tabular('time-train', time_train)
logger.record_tabular('time-eval', time_eval)
logger.record_tabular('time-sample', time_sample)
logger.record_tabular('time-total', time_total)
logger.record_tabular('epoch', epoch)
self.sampler.log_diagnostics()
logger.dump_tabular(with_prefix=False)
logger.pop_prefix()
def _init_training(self, env, policy, pool):
"""Method to be called at the start of training.
:param env: Environment instance.
:param policy: Policy instance.
:return: None
"""
self.env = env
if self._eval_n_episodes > 0:
self._eval_env = deep_clone(env)
self.policy = policy
self.pool = pool
def _init_training(self, env, policy, pool):
"""Method to be called at the start of training.
:param env: Environment instance.
:param policy: Policy instance.
:return: None
"""
self.env = env
if self._eval_n_episodes > 0:
self._eval_env = deep_clone(env)
self.policy = policy
self.pool = pool