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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
"""Tests for pg_train.
These tests excersize code paths available through configuration options.
Training will be run for just a few steps with the goal being to check that
nothing crashes.
"""
from absl import flags
import tensorflow as tf
from single_task import defaults # brain coder
from single_task import run # brain coder
FLAGS = flags.FLAGS
class TrainTest(tf.test.TestCase):
def RunTrainingSteps(self, config_string, num_steps=10):
"""Run a few training steps with the given config.
Just check that nothing crashes.
Args:
config_string: Config encoded in a string. See
$REPO_PATH/common/config_lib.py
num_steps: Number of training steps to run. Defaults to 10.
"""
config = defaults.default_config_with_updates(config_string)
FLAGS.master = ''
FLAGS.max_npe = num_steps * config.batch_size
FLAGS.summary_interval = 1
FLAGS.logdir = tf.test.get_temp_dir()
FLAGS.config = config_string
tf.reset_default_graph()
run.main(None)
def testVanillaPolicyGradient(self):
self.RunTrainingSteps(
'env=c(task="reverse"),'
'agent=c(algorithm="pg"),'
'timestep_limit=90,batch_size=64')
def testVanillaPolicyGradient_VariableLengthSequences(self):
self.RunTrainingSteps(
'env=c(task="reverse"),'
'agent=c(algorithm="pg",eos_token=False),'
'timestep_limit=90,batch_size=64')
def testVanillaActorCritic(self):
self.RunTrainingSteps(
'env=c(task="reverse"),'
'agent=c(algorithm="pg",ema_baseline_decay=0.0),'
'timestep_limit=90,batch_size=64')
def testPolicyGradientWithTopK(self):
self.RunTrainingSteps(
'env=c(task="reverse"),'
'agent=c(algorithm="pg",topk_loss_hparam=1.0,topk=10),'
'timestep_limit=90,batch_size=64')
def testVanillaActorCriticWithTopK(self):
self.RunTrainingSteps(
'env=c(task="reverse"),'
'agent=c(algorithm="pg",ema_baseline_decay=0.0,topk_loss_hparam=1.0,'
'topk=10),'
'timestep_limit=90,batch_size=64')
def testPolicyGradientWithTopK_VariableLengthSequences(self):
self.RunTrainingSteps(
'env=c(task="reverse"),'
'agent=c(algorithm="pg",topk_loss_hparam=1.0,topk=10,eos_token=False),'
'timestep_limit=90,batch_size=64')
def testPolicyGradientWithImportanceSampling(self):
self.RunTrainingSteps(
'env=c(task="reverse"),'
'agent=c(algorithm="pg",alpha=0.5),'
'timestep_limit=90,batch_size=64')
if __name__ == '__main__':
tf.test.main()
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