def summarize_rollout(self, training_info):
"""Generate summaries for rollout.
Note that training_info.info is empty here. Should use
training_info.rollout_info to generate the summaries.
Args:
training_info (TrainingInfo): TrainingInfo structure collected from
rollout.
Returns:
None
"""
if self._debug_summaries:
summary_utils.add_action_summaries(training_info.action,
self._action_spec,
"rollout_action")
self.add_reward_summary("rollout_reward/extrinsic",
training_info.reward)
if self._summarize_action_distributions:
field = nest_utils.find_field(training_info.rollout_info,
'action_distribution')
if len(field) == 1:
summary_utils.summarize_action_dist(
action_distributions=field[0],
action_specs=self._action_spec,
name="rollout_action_dist")
def summarize_train(self, training_info, loss_info, grads_and_vars):
"""Generate summaries for training & loss info.
For on-policy algorithms, training_info.info is available.
For off-policy alogirthms, both training_info.info and training_info.rollout_info
are available. However, the statistics for these two structure are for
the data batch sampled from the replay buffer. They do not represent
the statistics of current on-going rollout.
Args:
training_info (TrainingInfo): TrainingInfo structure collected from
rollout (on-policy training) or train_step (off-policy training).
loss_info (LossInfo): loss
grads_and_vars (tuple of (grad, var) pairs): list of gradients and
their corresponding variables
Returns:
None
"""
if self._summarize_grads_and_vars:
summary_utils.add_variables_summaries(grads_and_vars)
summary_utils.add_gradients_summaries(grads_and_vars)
if self._debug_summaries:
summary_utils.add_action_summaries(training_info.action,
self._action_spec)
summary_utils.add_loss_summaries(loss_info)
if self._summarize_action_distributions:
field = nest_utils.find_field(training_info.info,
'action_distribution')
if len(field) == 1:
summary_utils.summarize_action_dist(field[0],
self._action_spec)
def test_find_field(self):
nest = NTuple(a=1, b=NTuple(a=NTuple(a=2, b=3), b=2))
ret = nest_utils.find_field(nest, 'a')
self.assertEqual(len(ret), 2)
self.assertEqual(ret[0], nest.a)
self.assertEqual(ret[1], nest.b.a)
nest = (1, NTuple(a=NTuple(a=2, b=3), b=2))
ret = nest_utils.find_field(nest, 'a')
self.assertEqual(len(ret), 1)
self.assertEqual(ret[0], nest[1].a)
nest = NTuple(a=1, b=[NTuple(a=2, b=3), 2])
ret = nest_utils.find_field(nest, 'a')
self.assertEqual(len(ret), 2)
self.assertEqual(ret[0], nest.a)
self.assertEqual(ret[1], nest.b[0].a)