def test_weighted_generator(self):
data0 = types.Transition(np.array([[1], [2], [3]]), (), _REWARD, (),
())
it0 = iter([data0])
data1 = types.Transition(np.array([[4], [5], [6]]), (), _REWARD, (),
())
data2 = types.Transition(np.array([[7], [8], [9]]), (), _REWARD, (),
())
it1 = iter([
reverb.ReplaySample(info=reverb.SampleInfo(
*[() for _ in reverb.SampleInfo.tf_dtypes()]),
data=data1),
reverb.ReplaySample(info=reverb.SampleInfo(
*[() for _ in reverb.SampleInfo.tf_dtypes()]),
data=data2)
])
weighted_it = builder._generate_samples_with_demonstrations(
it0, it1, policy_to_expert_data_ratio=2, batch_size=3)
np.testing.assert_array_equal(
next(weighted_it).data.observation, np.array([[1], [4], [5]]))
np.testing.assert_array_equal(
next(weighted_it).data.observation, np.array([[7], [8], [2]]))
self.assertRaises(StopIteration, lambda: next(weighted_it))
def transition_dataset(environment: dm_env.Environment) -> tf.data.Dataset:
"""Fake dataset of Reverb N-step transition samples.
Args:
environment: Used to create a fake transition by looking at the
observation, action, discount and reward specs.
Returns:
tf.data.Dataset that produces the same fake N-step transition ReverSample
object indefinitely.
"""
observation = environment.observation_spec().generate_value()
action = environment.action_spec().generate_value()
reward = environment.reward_spec().generate_value()
discount = environment.discount_spec().generate_value()
data = (observation, action, reward, discount, observation)
key = np.array(0, np.uint64)
probability = np.array(1.0, np.float64)
table_size = np.array(1, np.int64)
priority = np.array(1.0, np.float64)
info = reverb.SampleInfo(key=key,
probability=probability,
table_size=table_size,
priority=priority)
sample = reverb.ReplaySample(info=info, data=data)
return tf.data.Dataset.from_tensors(sample).repeat()
def _n_step_transition_from_episode(observations: acme_types.NestedTensor,
actions: tf.Tensor,
rewards: tf.Tensor,
discounts: tf.Tensor,
n_step: int,
discount: float):
"""Produce Reverb-like N-step transition from a full episode.
Observations, actions, rewards and discounts have the same length. This
function will ignore the first reward and discount and the last action.
Args:
observations: [L, ...] Tensor.
actions: [L, ...] Tensor.
rewards: [L] Tensor.
discounts: [L] Tensor.
n_step: number of steps to squash into a single transition.
discount: discount to use for TD updates.
Returns:
(o_t, a_t, r_t, d_t, o_tp1) tuple.
"""
max_index = tf.shape(rewards)[0] - 1
first = tf.random.uniform(shape=(), minval=0, maxval=max_index - 1,
dtype=tf.int32)
last = tf.minimum(first + n_step, max_index)
o_t = tree.map_structure(operator.itemgetter(first), observations)
a_t = tree.map_structure(operator.itemgetter(first), actions)
o_tp1 = tree.map_structure(operator.itemgetter(last), observations)
# 0, 1, ..., n-1.
discount_range = tf.cast(tf.range(last - first), tf.float32)
# 1, g, ..., g^{n-1}.
additional_discounts = tf.pow(discount, discount_range)
# 1, d_t, d_t * d_{t+1}, ..., d_t * ... * d_{t+n-2}.
discounts = tf.concat([[1.], tf.math.cumprod(discounts[first:last-1])], 0)
# 1, g * d_t, ..., g^{n-1} * d_t * ... * d_{t+n-2}.
discounts *= additional_discounts
# r_t + g * d_t * r_{t+1} + ... + g^{n-1} * d_t * ... * d_{t+n-2} * r_{t+n-1}
# We have to shift rewards by one so last=max_index corresponds to transitions
# that include the last reward.
r_t = tf.reduce_sum(rewards[first+1:last+1] * discounts)
# g^{n-1} * d_{t} * ... * d_{t+n-1}.
d_t = discounts[-1]
key = tf.constant(0, tf.uint64)
probability = tf.constant(1.0, tf.float64)
table_size = tf.constant(1, tf.int64)
priority = tf.constant(1.0, tf.float64)
info = reverb.SampleInfo(
key=key,
probability=probability,
table_size=table_size,
priority=priority)
return reverb.ReplaySample(
info=info, data=acme_types.Transition(o_t, a_t, r_t, d_t, o_tp1))
def _sequence_from_episode(observations: acme_types.NestedTensor,
actions: tf.Tensor,
rewards: tf.Tensor,
discounts: tf.Tensor,
extra_spec: acme_types.NestedSpec,
period: int,
sequence_length: int):
"""Produce Reverb-like sequence from a full episode.
Observations, actions, rewards and discounts have the same length. This
function will ignore the first reward and discount and the last action.
This function generates fake (all-zero) extras.
See docs for reverb.SequenceAdder() for more details.
Args:
observations: [L, ...] Tensor.
actions: [L, ...] Tensor.
rewards: [L] Tensor.
discounts: [L] Tensor.
extra_spec: A possibly nested structure of specs for extras. This function
will generate fake (all-zero) extras.
period: The period with which we add sequences.
sequence_length: The fixed length of sequences we wish to add.
Returns:
(o_t, a_t, r_t, d_t, e_t) Tuple.
"""
length = tf.shape(rewards)[0]
first = tf.random.uniform(shape=(), minval=0, maxval=length, dtype=tf.int32)
first = first // period * period # Get a multiple of `period`.
to = tf.minimum(first + sequence_length, length)
def _slice_and_pad(x):
pad_length = sequence_length + first - to
padding_shape = tf.concat([[pad_length], tf.shape(x)[1:]], axis=0)
result = tf.concat([x[first:to], tf.zeros(padding_shape, x.dtype)], axis=0)
result.set_shape([sequence_length] + x.shape.as_list()[1:])
return result
o_t = tree.map_structure(_slice_and_pad, observations)
a_t = tree.map_structure(_slice_and_pad, actions)
r_t = _slice_and_pad(rewards)
d_t = _slice_and_pad(discounts)
def _sequence_zeros(spec):
return tf.zeros([sequence_length] + spec.shape, spec.dtype)
e_t = tree.map_structure(_sequence_zeros, extra_spec)
key = tf.zeros([sequence_length], tf.uint64)
probability = tf.ones([sequence_length], tf.float64)
table_size = tf.ones([sequence_length], tf.int64)
info = reverb.SampleInfo(
key=key, probability=probability, table_size=table_size)
return reverb.ReplaySample(info=info, data=(o_t, a_t, r_t, d_t, e_t))
class ReverbUtilsTest(absltest.TestCase):
def test_make_replay_table_preserves_table_info(self):
limiter = reverb.rate_limiters.SampleToInsertRatio(
samples_per_insert=1, min_size_to_sample=2, error_buffer=(0, 10))
table = reverb.Table(
name='test',
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
max_size=10,
rate_limiter=limiter)
new_table = reverb_utils.make_replay_table_from_info(table.info)
new_info = new_table.info
# table_worker_time is not set by the above utility since this is meant to
# be monitoring information about any given table. So instead we copy this
# so that the assertion below checks that everything else matches.
new_info.table_worker_time.sleeping_ms = (
table.info.table_worker_time.sleeping_ms)
self.assertEqual(new_info, table.info)
_EMPTY_INFO = reverb.SampleInfo(*[() for _ in reverb.SampleInfo.tf_dtypes()])
_DUMMY_OBS = np.array([[[0], [1], [2]]])
_DUMMY_ACTION = np.array([[[3], [4], [5]]])
_DUMMY_REWARD = np.array([[6, 7, 8]])
_DUMMY_DISCOUNT = np.array([[.99, .99, .99]])
_DUMMY_NEXT_OBS = np.array([[[1], [2], [0]]])
_DUMMY_RETURN = np.array([[20.77, 14.92, 8.]])
def _create_dummy_steps(self):
return reverb_adders.Step(
observation=self._DUMMY_OBS,
action=self._DUMMY_ACTION,
reward=self._DUMMY_REWARD,
discount=self._DUMMY_DISCOUNT,
start_of_episode=True,
extras={'return': self._DUMMY_RETURN})
def _create_dummy_transitions(self):
return types.Transition(
observation=self._DUMMY_OBS,
action=self._DUMMY_ACTION,
reward=self._DUMMY_REWARD,
discount=self._DUMMY_DISCOUNT,
next_observation=self._DUMMY_NEXT_OBS,
extras={'return': self._DUMMY_RETURN})
def test_replay_sample_to_sars_transition_is_sequence(self):
fake_sample = reverb.ReplaySample(
info=self._EMPTY_INFO, data=self._create_dummy_steps())
fake_transition = self._create_dummy_transitions()
transition_from_sample = reverb_utils.replay_sample_to_sars_transition(
fake_sample, is_sequence=True)
tree.map_structure(np.testing.assert_array_equal, transition_from_sample,
fake_transition)
def _build_sequence_example(sequences):
"""Convert raw sequences into a Reverb sequence sample."""
o = sequences['observation']
a = sequences['action']
r = sequences['reward']
p = sequences['discount']
info = reverb.SampleInfo(key=tf.constant(0, tf.uint64),
probability=tf.constant(1.0, tf.float64),
table_size=tf.constant(0, tf.int64),
priority=tf.constant(1.0, tf.float64))
return reverb.ReplaySample(info=info, data=(o, a, r, p))
def _build_sequence_example(sequences):
"""Convert raw sequences into a Reverb sequence sample."""
data = adders.Step(observation=sequences['observation'],
action=sequences['action'],
reward=sequences['reward'],
discount=sequences['discount'],
start_of_episode=(),
extras=())
info = reverb.SampleInfo(key=tf.constant(0, tf.uint64),
probability=tf.constant(1.0, tf.float64),
table_size=tf.constant(0, tf.int64),
priority=tf.constant(1.0, tf.float64))
return reverb.ReplaySample(info=info, data=data)
def _build_sarsa_example(sequences):
"""Convert raw sequences into a Reverb n-step SARSA sample."""
o_tm1 = tree.map_structure(lambda t: t[0], sequences['observation'])
o_t = tree.map_structure(lambda t: t[1], sequences['observation'])
a_tm1 = tree.map_structure(lambda t: t[0], sequences['action'])
a_t = tree.map_structure(lambda t: t[1], sequences['action'])
r_t = tree.map_structure(lambda t: t[0], sequences['reward'])
p_t = tree.map_structure(lambda t: t[0], sequences['discount'])
info = reverb.SampleInfo(key=tf.constant(0, tf.uint64),
probability=tf.constant(1.0, tf.float64),
table_size=tf.constant(0, tf.int64),
priority=tf.constant(1.0, tf.float64))
return reverb.ReplaySample(info=info,
data=(o_tm1, a_tm1, r_t, p_t, o_t, a_t))
class ReverbUtilsTest(absltest.TestCase):
def test_make_replay_table_preserves_table_info(self):
limiter = reverb.rate_limiters.SampleToInsertRatio(
samples_per_insert=1, min_size_to_sample=2, error_buffer=(0, 10))
table = reverb.Table(
name='test',
sampler=reverb.selectors.Uniform(),
remover=reverb.selectors.Fifo(),
max_size=10,
rate_limiter=limiter)
table_from_info = reverb_utils.make_replay_table_from_info(table.info)
self.assertEqual(table_from_info.info, table.info)
_EMPTY_INFO = reverb.SampleInfo((), (), (), ())
_DUMMY_OBS = np.array([[[0], [1], [2]]])
_DUMMY_ACTION = np.array([[[3], [4], [5]]])
_DUMMY_REWARD = np.array([[6, 7, 8]])
_DUMMY_DISCOUNT = np.array([[.99, .99, .99]])
_DUMMY_NEXT_OBS = np.array([[[1], [2], [0]]])
def _create_dummy_steps(self):
return reverb_adders.Step(
observation=self._DUMMY_OBS,
action=self._DUMMY_ACTION,
reward=self._DUMMY_REWARD,
discount=self._DUMMY_DISCOUNT,
start_of_episode=True,
extras=())
def _create_dummy_transitions(self):
return types.Transition(
observation=self._DUMMY_OBS,
action=self._DUMMY_ACTION,
reward=self._DUMMY_REWARD,
discount=self._DUMMY_DISCOUNT,
next_observation=self._DUMMY_NEXT_OBS)
def test_replay_sample_to_sars_transition_is_sequence(self):
fake_sample = reverb.ReplaySample(
info=self._EMPTY_INFO, data=self._create_dummy_steps())
fake_transition = self._create_dummy_transitions()
transition_from_sample = reverb_utils.replay_sample_to_sars_transition(
fake_sample, is_sequence=True)
tree.map_structure(np.testing.assert_array_equal, transition_from_sample,
fake_transition)
def test_after_train_step_fn_with_fresh_data_only(self,
create_strategy_fn):
strategy = create_strategy_fn()
with strategy.scope():
# Prepare the test context context.
train_step = train_utils.create_train_step()
train_step.assign(225)
train_steps_per_policy_update = 100
# Create the after train function to test, and the test input.
after_train_step_fn = (
train_utils.create_staleness_metrics_after_train_step_fn(
train_step,
train_steps_per_policy_update=train_steps_per_policy_update
))
observation_train_steps = np.array([[200], [200], [200]],
dtype=np.int64)
# Define the expectations (expected scalar summary calls).
expected_scalar_summary_calls = [
mock.call(name='staleness/max_train_step_delta_in_batch',
data=0,
step=225),
mock.call(name='staleness/max_policy_update_delta_in_batch',
data=0,
step=225),
mock.call(name='staleness/num_stale_obserations_in_batch',
data=0,
step=225)
]
# Call the after train function and check the expectations.
with mock.patch.object(tf.summary, 'scalar',
autospec=True) as mock_scalar_summary:
# Call the `after_train_function` on the test input. Assumed the
# observation train steps are stored in the field `priority` of the
# the sample info of Reverb.
info = reverb.SampleInfo(
*[None for _ in reverb.SampleInfo.tf_dtypes()])
info = info._replace(priority=observation_train_steps)
strategy.run(after_train_step_fn, args=((None, info), None))
# Check if the expected calls happened on the scalar summary.
mock_scalar_summary.assert_has_calls(
expected_scalar_summary_calls, any_order=False)
def _make_reverb_sample(o_t, a_t, r_t, d_t, o_tp1, a_tp1, extras):
"""Create Reverb sample with offline data.
Args:
o_t: Observation at time t.
a_t: Action at time t.
r_t: Reward at time t.
d_t: Discount at time t.
o_tp1: Observation at time t+1.
a_tp1: Action at time t+1.
extras: Dictionary with extra features.
Returns:
Replay sample with fake info: key=0, probability=1, table_size=0.
"""
info = reverb.SampleInfo(key=tf.constant(0, tf.uint64),
probability=tf.constant(1.0, tf.float64),
table_size=tf.constant(0, tf.int64),
priority=tf.constant(1.0, tf.float64))
data = (o_t, a_t, r_t, d_t, o_tp1, a_tp1, extras)
return reverb.ReplaySample(info=info, data=data)
def add_info_fn(data):
info = reverb.SampleInfo(key=0,
probability=0.0,
table_size=0,
priority=0.0)
return reverb.ReplaySample(info=info, data=data)
def _reverb_sample(*data_tuple):
info = reverb.SampleInfo(key=tf.constant(0, tf.uint64),
probability=tf.constant(1.0, tf.float64),
table_size=tf.constant(0, tf.int64),
priority=tf.constant(1.0, tf.float64))
return reverb.ReplaySample(info=info, data=data_tuple)
