def test_tree_set_overlap(self):
tree = SumSegmentTree(4)
tree[2] = 1.0
tree[2] = 3.0
assert np.isclose(tree.sum(), 3.0)
assert np.isclose(tree.sum(2, 3), 3.0)
assert np.isclose(tree.sum(2, -1), 3.0)
assert np.isclose(tree.sum(2, 4), 3.0)
assert np.isclose(tree.sum(2), 3.0)
assert np.isclose(tree.sum(1, 2), 0.0)
def test_tree_set(self):
tree = SumSegmentTree(4)
tree[2] = 1.0
tree[3] = 3.0
assert np.isclose(tree.sum(), 4.0)
assert np.isclose(tree.sum(0, 2), 0.0)
assert np.isclose(tree.sum(0, 3), 1.0)
assert np.isclose(tree.sum(2, 3), 1.0)
assert np.isclose(tree.sum(2, -1), 1.0)
assert np.isclose(tree.sum(2, 4), 4.0)
assert np.isclose(tree.sum(2), 4.0)
class PrioritizedReplayBuffer(ReplayBuffer):
@DeveloperAPI
def __init__(self, size, alpha):
"""Create Prioritized Replay buffer.
Parameters
----------
size: int
Max number of transitions to store in the buffer. When the buffer
overflows the old memories are dropped.
alpha: float
how much prioritization is used
(0 - no prioritization, 1 - full prioritization)
See Also
--------
ReplayBuffer.__init__
"""
super(PrioritizedReplayBuffer, self).__init__(size)
assert alpha > 0
self._alpha = alpha
it_capacity = 1
while it_capacity < size:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
self._prio_change_stats = WindowStat("reprio", 1000)
@DeveloperAPI
def add(self, obs_t, action, reward, obs_tp1, done, weight):
"""See ReplayBuffer.store_effect"""
idx = self._next_idx
super(PrioritizedReplayBuffer, self).add(obs_t, action, reward,
obs_tp1, done, weight)
if weight is None:
weight = self._max_priority
self._it_sum[idx] = weight**self._alpha
self._it_min[idx] = weight**self._alpha
def _sample_proportional(self, batch_size):
res = []
for _ in range(batch_size):
# TODO(szymon): should we ensure no repeats?
mass = random.random() * self._it_sum.sum(0, len(self._storage))
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
@DeveloperAPI
def sample_idxes(self, batch_size):
return self._sample_proportional(batch_size)
@DeveloperAPI
def sample_with_idxes(self, idxes, beta):
assert beta > 0
self._num_sampled += len(idxes)
weights = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage))**(-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self._storage))**(-beta)
weights.append(weight / max_weight)
weights = np.array(weights)
encoded_sample = self._encode_sample(idxes)
return tuple(list(encoded_sample) + [weights, idxes])
@DeveloperAPI
def sample(self, batch_size, beta):
"""Sample a batch of experiences.
compared to ReplayBuffer.sample
it also returns importance weights and idxes
of sampled experiences.
Parameters
----------
batch_size: int
How many transitions to sample.
beta: float
To what degree to use importance weights
(0 - no corrections, 1 - full correction)
Returns
-------
obs_batch: np.array
batch of observations
act_batch: np.array
batch of actions executed given obs_batch
rew_batch: np.array
rewards received as results of executing act_batch
next_obs_batch: np.array
next set of observations seen after executing act_batch
done_mask: np.array
done_mask[i] = 1 if executing act_batch[i] resulted in
the end of an episode and 0 otherwise.
weights: np.array
Array of shape (batch_size,) and dtype np.float32
denoting importance weight of each sampled transition
idxes: np.array
Array of shape (batch_size,) and dtype np.int32
idexes in buffer of sampled experiences
"""
assert beta >= 0.0
self._num_sampled += batch_size
idxes = self._sample_proportional(batch_size)
weights = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage))**(-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self._storage))**(-beta)
weights.append(weight / max_weight)
weights = np.array(weights)
encoded_sample = self._encode_sample(idxes)
return tuple(list(encoded_sample) + [weights, idxes])
@DeveloperAPI
def update_priorities(self, idxes, priorities):
"""Update priorities of sampled transitions.
sets priority of transition at index idxes[i] in buffer
to priorities[i].
Parameters
----------
idxes: [int]
List of idxes of sampled transitions
priorities: [float]
List of updated priorities corresponding to
transitions at the sampled idxes denoted by
variable `idxes`.
"""
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
assert priority > 0
assert 0 <= idx < len(self._storage)
delta = priority**self._alpha - self._it_sum[idx]
self._prio_change_stats.push(delta)
self._it_sum[idx] = priority**self._alpha
self._it_min[idx] = priority**self._alpha
self._max_priority = max(self._max_priority, priority)
@DeveloperAPI
def stats(self, debug=False):
parent = ReplayBuffer.stats(self, debug)
if debug:
parent.update(self._prio_change_stats.stats())
return parent
class PrioritizedReplayBuffer(ReplayBuffer):
@DeveloperAPI
def __init__(self, size: int, alpha: float):
"""Create Prioritized Replay buffer.
Args:
size (int): Max number of items to store in the FIFO buffer.
alpha (float): how much prioritization is used
(0 - no prioritization, 1 - full prioritization).
See also:
ReplayBuffer.__init__()
"""
super(PrioritizedReplayBuffer, self).__init__(size)
assert alpha > 0
self._alpha = alpha
it_capacity = 1
while it_capacity < size:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
self._prio_change_stats = WindowStat("reprio", 1000)
@DeveloperAPI
def add(self, item: SampleBatchType, weight: float):
idx = self._next_idx
super(PrioritizedReplayBuffer, self).add(item, weight)
if weight is None:
weight = self._max_priority
self._it_sum[idx] = weight**self._alpha
self._it_min[idx] = weight**self._alpha
def _sample_proportional(self, num_items: int):
res = []
for _ in range(num_items):
# TODO(szymon): should we ensure no repeats?
mass = random.random() * self._it_sum.sum(0, len(self._storage))
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
@DeveloperAPI
def sample(self, num_items: int, beta: float) -> SampleBatchType:
"""Sample a batch of experiences and return priority weights, indices.
Args:
num_items (int): Number of items to sample from this buffer.
beta (float): To what degree to use importance weights
(0 - no corrections, 1 - full correction).
Returns:
SampleBatchType: Concatenated batch of items including "weights"
and "batch_indexes" fields denoting IS of each sampled
transition and original idxes in buffer of sampled experiences.
"""
assert beta >= 0.0
self._num_sampled += num_items
idxes = self._sample_proportional(num_items)
weights = []
batch_indexes = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage))**(-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self._storage))**(-beta)
count = self._storage[idx].count
weights.extend([weight / max_weight] * count)
batch_indexes.extend([idx] * count)
batch = self._encode_sample(idxes)
# Note: prioritization is not supported in lockstep replay mode.
if isinstance(batch, SampleBatch):
assert len(weights) == batch.count
assert len(batch_indexes) == batch.count
batch["weights"] = np.array(weights)
batch["batch_indexes"] = np.array(batch_indexes)
return batch
@DeveloperAPI
def update_priorities(self, idxes, priorities):
"""Update priorities of sampled transitions.
sets priority of transition at index idxes[i] in buffer
to priorities[i].
Parameters
----------
idxes: [int]
List of idxes of sampled transitions
priorities: [float]
List of updated priorities corresponding to
transitions at the sampled idxes denoted by
variable `idxes`.
"""
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
assert priority > 0
assert 0 <= idx < len(self._storage)
delta = priority**self._alpha - self._it_sum[idx]
self._prio_change_stats.push(delta)
self._it_sum[idx] = priority**self._alpha
self._it_min[idx] = priority**self._alpha
self._max_priority = max(self._max_priority, priority)
@DeveloperAPI
def stats(self, debug=False):
parent = ReplayBuffer.stats(self, debug)
if debug:
parent.update(self._prio_change_stats.stats())
return parent
class PrioritizedReplayBuffer(ReplayBuffer):
"""This buffer implements Prioritized Experience Replay
The algorithm has been described by Tom Schaul et. al. in "Prioritized
Experience Replay". See https://arxiv.org/pdf/1511.05952.pdf for
the full paper.
"""
@ExperimentalAPI
def __init__(self,
capacity: int = 10000,
storage_unit: str = "timesteps",
alpha: float = 1.0,
**kwargs):
"""Initializes a PrioritizedReplayBuffer instance.
Args:
capacity: Max number of timesteps to store in the FIFO
buffer. After reaching this number, older samples will be
dropped to make space for new ones.
storage_unit: Either 'timesteps', 'sequences' or
'episodes'. Specifies how experiences are stored.
alpha: How much prioritization is used
(0.0=no prioritization, 1.0=full prioritization).
**kwargs: Forward compatibility kwargs.
"""
ReplayBuffer.__init__(self, capacity, storage_unit, **kwargs)
assert alpha > 0
self._alpha = alpha
# Segment tree must have capacity that is a power of 2
it_capacity = 1
while it_capacity < self.capacity:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
self._prio_change_stats = WindowStat("reprio", 1000)
@ExperimentalAPI
@override(ReplayBuffer)
def _add_single_batch(self, item: SampleBatchType, **kwargs) -> None:
"""Add a batch of experiences to self._storage with weight.
An item consists of either one or more timesteps, a sequence or an
episode. Differs from add() in that it does not consider the storage
unit or type of batch and simply stores it.
Args:
item: The item to be added.
**kwargs: Forward compatibility kwargs.
"""
weight = kwargs.get("weight", None)
if weight is None:
weight = self._max_priority
self._it_sum[self._next_idx] = weight**self._alpha
self._it_min[self._next_idx] = weight**self._alpha
ReplayBuffer._add_single_batch(self, item)
def _sample_proportional(self, num_items: int) -> List[int]:
res = []
for _ in range(num_items):
# TODO(szymon): should we ensure no repeats?
mass = random.random() * self._it_sum.sum(0, len(self._storage))
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
@ExperimentalAPI
@override(ReplayBuffer)
def sample(self, num_items: int, beta: float,
**kwargs) -> Optional[SampleBatchType]:
"""Sample `num_items` items from this buffer, including prio. weights.
Samples in the results may be repeated.
Examples for storage of SamplesBatches:
- If storage unit `timesteps` has been chosen and batches of
size 5 have been added, sample(5) will yield a concatenated batch of
15 timesteps.
- If storage unit 'sequences' has been chosen and sequences of
different lengths have been added, sample(5) will yield a concatenated
batch with a number of timesteps equal to the sum of timesteps in
the 5 sampled sequences.
- If storage unit 'episodes' has been chosen and episodes of
different lengths have been added, sample(5) will yield a concatenated
batch with a number of timesteps equal to the sum of timesteps in
the 5 sampled episodes.
Args:
num_items: Number of items to sample from this buffer.
beta: To what degree to use importance weights
(0 - no corrections, 1 - full correction).
**kwargs: Forward compatibility kwargs.
Returns:
Concatenated SampleBatch of items including "weights" and
"batch_indexes" fields denoting IS of each sampled
transition and original idxes in buffer of sampled experiences.
"""
assert beta >= 0.0
idxes = self._sample_proportional(num_items)
weights = []
batch_indexes = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self))**(-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self))**(-beta)
count = self._storage[idx].count
# If zero-padded, count will not be the actual batch size of the
# data.
if (isinstance(self._storage[idx], SampleBatch)
and self._storage[idx].zero_padded):
actual_size = self._storage[idx].max_seq_len
else:
actual_size = count
weights.extend([weight / max_weight] * actual_size)
batch_indexes.extend([idx] * actual_size)
self._num_timesteps_sampled += count
batch = self._encode_sample(idxes)
# Note: prioritization is not supported in multi agent lockstep
if isinstance(batch, SampleBatch):
batch["weights"] = np.array(weights)
batch["batch_indexes"] = np.array(batch_indexes)
return batch
@ExperimentalAPI
def update_priorities(self, idxes: List[int],
priorities: List[float]) -> None:
"""Update priorities of items at given indices.
Sets priority of item at index idxes[i] in buffer
to priorities[i].
Args:
idxes: List of indices of items
priorities: List of updated priorities corresponding to
items at the idxes denoted by variable `idxes`.
"""
# Making sure we don't pass in e.g. a torch tensor.
assert isinstance(
idxes, (list, np.ndarray)
), "ERROR: `idxes` is not a list or np.ndarray, but " "{}!".format(
type(idxes).__name__)
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
assert priority > 0
assert 0 <= idx < len(self._storage)
delta = priority**self._alpha - self._it_sum[idx]
self._prio_change_stats.push(delta)
self._it_sum[idx] = priority**self._alpha
self._it_min[idx] = priority**self._alpha
self._max_priority = max(self._max_priority, priority)
@ExperimentalAPI
@override(ReplayBuffer)
def stats(self, debug: bool = False) -> Dict:
"""Returns the stats of this buffer.
Args:
debug: If true, adds sample eviction statistics to the
returned stats dict.
Returns:
A dictionary of stats about this buffer.
"""
parent = ReplayBuffer.stats(self, debug)
if debug:
parent.update(self._prio_change_stats.stats())
return parent
@ExperimentalAPI
@override(ReplayBuffer)
def get_state(self) -> Dict[str, Any]:
"""Returns all local state.
Returns:
The serializable local state.
"""
# Get parent state.
state = super().get_state()
# Add prio weights.
state.update({
"sum_segment_tree": self._it_sum.get_state(),
"min_segment_tree": self._it_min.get_state(),
"max_priority": self._max_priority,
})
return state
@ExperimentalAPI
@override(ReplayBuffer)
def set_state(self, state: Dict[str, Any]) -> None:
"""Restores all local state to the provided `state`.
Args:
state: The new state to set this buffer. Can be obtained by
calling `self.get_state()`.
"""
super().set_state(state)
self._it_sum.set_state(state["sum_segment_tree"])
self._it_min.set_state(state["min_segment_tree"])
self._max_priority = state["max_priority"]
class PrioritizedReplayBuffer(ReplayBuffer):
@ExperimentalAPI
def __init__(
self,
capacity: int = 10000,
storage_unit: str = "timesteps",
alpha: float = 1.0,
):
"""Initializes a PrioritizedReplayBuffer instance.
Args:
capacity: Max number of timesteps to store in the FIFO
buffer. After reaching this number, older samples will be
dropped to make space for new ones.
storage_unit: Either 'sequences' or 'timesteps'. Specifies how
experiences are stored.
alpha: How much prioritization is used
(0.0=no prioritization, 1.0=full prioritization).
"""
ReplayBuffer.__init__(self, capacity, storage_unit)
assert alpha > 0
self._alpha = alpha
it_capacity = 1
while it_capacity < self.capacity:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
self._prio_change_stats = WindowStat("reprio", 1000)
@ExperimentalAPI
@override(ReplayBuffer)
def add(self, batch: SampleBatchType, weight: float) -> None:
"""Add a batch of experiences.
Args:
batch: SampleBatch to add to this buffer's storage.
weight: The weight of the added sample used in subsequent sampling
steps.
"""
idx = self._next_idx
assert batch.count > 0, batch
warn_replay_capacity(item=batch, num_items=self.capacity / batch.count)
# Update our timesteps counts.
self._num_timesteps_added += batch.count
self._num_timesteps_added_wrap += batch.count
if self._next_idx >= len(self._storage):
self._storage.append(batch)
self._est_size_bytes += batch.size_bytes()
else:
self._storage[self._next_idx] = batch
# Wrap around storage as a circular buffer once we hit capacity.
if self._num_timesteps_added_wrap >= self.capacity:
self._eviction_started = True
self._num_timesteps_added_wrap = 0
self._next_idx = 0
else:
self._next_idx += 1
# Eviction of older samples has already started (buffer is "full").
if self._eviction_started:
self._evicted_hit_stats.push(self._hit_count[self._next_idx])
self._hit_count[self._next_idx] = 0
if weight is None:
weight = self._max_priority
self._it_sum[idx] = weight**self._alpha
self._it_min[idx] = weight**self._alpha
def _sample_proportional(self, num_items: int) -> List[int]:
res = []
for _ in range(num_items):
# TODO(szymon): should we ensure no repeats?
mass = random.random() * self._it_sum.sum(0, len(self._storage))
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
@ExperimentalAPI
@override(ReplayBuffer)
def sample(self, num_items: int, beta: float) -> Optional[SampleBatchType]:
"""Sample `num_items` items from this buffer, including prio. weights.
If less than `num_items` records are in this buffer, some samples in
the results may be repeated to fulfil the batch size (`num_items`)
request.
Args:
num_items: Number of items to sample from this buffer.
beta: To what degree to use importance weights
(0 - no corrections, 1 - full correction).
Returns:
Concatenated batch of items including "weights" and
"batch_indexes" fields denoting IS of each sampled
transition and original idxes in buffer of sampled experiences.
"""
# If we don't have any samples yet in this buffer, return None.
if len(self) == 0:
return None
assert beta >= 0.0
idxes = self._sample_proportional(num_items)
weights = []
batch_indexes = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self))**(-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self))**(-beta)
count = self._storage[idx].count
# If zero-padded, count will not be the actual batch size of the
# data.
if (isinstance(self._storage[idx], SampleBatch)
and self._storage[idx].zero_padded):
actual_size = self._storage[idx].max_seq_len
else:
actual_size = count
weights.extend([weight / max_weight] * actual_size)
batch_indexes.extend([idx] * actual_size)
self._num_timesteps_sampled += count
batch = self._encode_sample(idxes)
# Note: prioritization is not supported in lockstep replay mode.
if isinstance(batch, SampleBatch):
batch["weights"] = np.array(weights)
batch["batch_indexes"] = np.array(batch_indexes)
return batch
@ExperimentalAPI
def update_priorities(self, idxes: List[int],
priorities: List[float]) -> None:
"""Update priorities of sampled transitions.
Sets priority of transition at index idxes[i] in buffer
to priorities[i].
Args:
idxes: List of indices of sampled transitions
priorities: List of updated priorities corresponding to
transitions at the sampled idxes denoted by
variable `idxes`.
"""
# Making sure we don't pass in e.g. a torch tensor.
assert isinstance(
idxes, (list, np.ndarray)
), "ERROR: `idxes` is not a list or np.ndarray, but " "{}!".format(
type(idxes).__name__)
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
assert priority > 0
assert 0 <= idx < len(self._storage)
delta = priority**self._alpha - self._it_sum[idx]
self._prio_change_stats.push(delta)
self._it_sum[idx] = priority**self._alpha
self._it_min[idx] = priority**self._alpha
self._max_priority = max(self._max_priority, priority)
@ExperimentalAPI
@override(ReplayBuffer)
def stats(self, debug: bool = False) -> Dict:
"""Returns the stats of this buffer.
Args:
debug: If true, adds sample eviction statistics to the
returned stats dict.
Returns:
A dictionary of stats about this buffer.
"""
parent = ReplayBuffer.stats(self, debug)
if debug:
parent.update(self._prio_change_stats.stats())
return parent
@ExperimentalAPI
@override(ReplayBuffer)
def get_state(self) -> Dict[str, Any]:
"""Returns all local state.
Returns:
The serializable local state.
"""
# Get parent state.
state = super().get_state()
# Add prio weights.
state.update({
"sum_segment_tree": self._it_sum.get_state(),
"min_segment_tree": self._it_min.get_state(),
"max_priority": self._max_priority,
})
return state
@ExperimentalAPI
@override(ReplayBuffer)
def set_state(self, state: Dict[str, Any]) -> None:
"""Restores all local state to the provided `state`.
Args:
state: The new state to set this buffer. Can be obtained by
calling `self.get_state()`.
"""
super().set_state(state)
self._it_sum.set_state(state["sum_segment_tree"])
self._it_min.set_state(state["min_segment_tree"])
self._max_priority = state["max_priority"]
class PrioritizedReplayBuffer(ReplayBuffer):
@DeveloperAPI
def __init__(self,
capacity: int = 10000,
alpha: float = 1.0,
size: Optional[int] = DEPRECATED_VALUE):
"""Initializes a PrioritizedReplayBuffer instance.
Args:
capacity (int): Max number of timesteps to store in the FIFO
buffer. After reaching this number, older samples will be
dropped to make space for new ones.
alpha (float): How much prioritization is used
(0.0=no prioritization, 1.0=full prioritization).
"""
super(PrioritizedReplayBuffer, self).__init__(capacity, size)
assert alpha > 0
self._alpha = alpha
it_capacity = 1
while it_capacity < self.capacity:
it_capacity *= 2
self._it_sum = SumSegmentTree(it_capacity)
self._it_min = MinSegmentTree(it_capacity)
self._max_priority = 1.0
self._prio_change_stats = WindowStat("reprio", 1000)
@DeveloperAPI
@override(ReplayBuffer)
def add(self, item: SampleBatchType, weight: float) -> None:
idx = self._next_idx
super(PrioritizedReplayBuffer, self).add(item, weight)
if weight is None:
weight = self._max_priority
self._it_sum[idx] = weight**self._alpha
self._it_min[idx] = weight**self._alpha
def _sample_proportional(self, num_items: int) -> List[int]:
res = []
for _ in range(num_items):
# TODO(szymon): should we ensure no repeats?
mass = random.random() * self._it_sum.sum(0, len(self._storage))
idx = self._it_sum.find_prefixsum_idx(mass)
res.append(idx)
return res
@DeveloperAPI
@override(ReplayBuffer)
def sample(self, num_items: int, beta: float) -> SampleBatchType:
"""Sample a batch of experiences and return priority weights, indices.
Args:
num_items (int): Number of items to sample from this buffer.
beta (float): To what degree to use importance weights
(0 - no corrections, 1 - full correction).
Returns:
SampleBatchType: Concatenated batch of items including "weights"
and "batch_indexes" fields denoting IS of each sampled
transition and original idxes in buffer of sampled experiences.
"""
assert beta >= 0.0
idxes = self._sample_proportional(num_items)
weights = []
batch_indexes = []
p_min = self._it_min.min() / self._it_sum.sum()
max_weight = (p_min * len(self._storage))**(-beta)
for idx in idxes:
p_sample = self._it_sum[idx] / self._it_sum.sum()
weight = (p_sample * len(self._storage))**(-beta)
count = self._storage[idx].count
# If zero-padded, count will not be the actual batch size of the
# data.
if isinstance(self._storage[idx], SampleBatch) and \
self._storage[idx].zero_padded:
actual_size = self._storage[idx].max_seq_len
else:
actual_size = count
weights.extend([weight / max_weight] * actual_size)
batch_indexes.extend([idx] * actual_size)
self._num_timesteps_sampled += count
batch = self._encode_sample(idxes)
# Note: prioritization is not supported in lockstep replay mode.
if isinstance(batch, SampleBatch):
batch["weights"] = np.array(weights)
batch["batch_indexes"] = np.array(batch_indexes)
return batch
@DeveloperAPI
def update_priorities(self, idxes: List[int],
priorities: List[float]) -> None:
"""Update priorities of sampled transitions.
sets priority of transition at index idxes[i] in buffer
to priorities[i].
Parameters
----------
idxes: [int]
List of idxes of sampled transitions
priorities: [float]
List of updated priorities corresponding to
transitions at the sampled idxes denoted by
variable `idxes`.
"""
# Making sure we don't pass in e.g. a torch tensor.
assert isinstance(idxes, (list, np.ndarray)), \
"ERROR: `idxes` is not a list or np.ndarray, but " \
"{}!".format(type(idxes).__name__)
assert len(idxes) == len(priorities)
for idx, priority in zip(idxes, priorities):
assert priority > 0
assert 0 <= idx < len(self._storage)
delta = priority**self._alpha - self._it_sum[idx]
self._prio_change_stats.push(delta)
self._it_sum[idx] = priority**self._alpha
self._it_min[idx] = priority**self._alpha
self._max_priority = max(self._max_priority, priority)
@DeveloperAPI
@override(ReplayBuffer)
def stats(self, debug: bool = False) -> Dict:
parent = ReplayBuffer.stats(self, debug)
if debug:
parent.update(self._prio_change_stats.stats())
return parent
@DeveloperAPI
@override(ReplayBuffer)
def get_state(self) -> Dict[str, Any]:
"""Returns all local state.
Returns:
Dict[str, Any]: The serializable local state.
"""
# Get parent state.
state = super().get_state()
# Add prio weights.
state.update({
"sum_segment_tree": self._it_sum.get_state(),
"min_segment_tree": self._it_min.get_state(),
"max_priority": self._max_priority,
})
return state
@DeveloperAPI
@override(ReplayBuffer)
def set_state(self, state: Dict[str, Any]) -> None:
"""Restores all local state to the provided `state`.
Args:
state (Dict[str, Any]): The new state to set this buffer. Can be
obtained by calling `self.get_state()`.
"""
super().set_state(state)
self._it_sum.set_state(state["sum_segment_tree"])
self._it_min.set_state(state["min_segment_tree"])
self._max_priority = state["max_priority"]