reference/generated/torchrl.data.RateLimitedReplayBuffer

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RateLimitedReplayBuffer

class torchrl.data.RateLimitedReplayBuffer(*args, use_ray_service=False, service_backend=None, service_backend_options=None, **kwargs)

A replay buffer with a cumulative sample-to-insert ratio limit.

RateLimitedReplayBuffer permits at most samples_per_insert sampled records for every record written through add() or extend(). The limit uses the replay buffer’s cumulative counters rather than its current occupancy, so it remains meaningful after circular storage wraps. Concurrent callers reserve sample budget atomically.

This class deliberately controls only replay sampling. Policy-version freshness is handled by samplers such as StalenessAwareSampler, while weight publication is owned by collectors and trainer weight-update hooks.

Keyword Arguments:

• samples_per_insert (float) – maximum cumulative number of sampled records per inserted record. Must be finite and positive.

• **kwargs – forwarded to ReplayBuffer. Prefetching is not supported because prefetched samples would consume ratio budget before the corresponding call reserves it.

Examples

>>> import torch
>>> from torchrl.data import LazyTensorStorage, RateLimitedReplayBuffer
>>> replay = RateLimitedReplayBuffer(
...     storage=LazyTensorStorage(8),
...     batch_size=2,
...     samples_per_insert=1.0,
... )
>>> _ = replay.extend(torch.arange(2))
>>> replay.sample(wait=True, timeout=1.0).shape
torch.Size([2])
>>> replay.can_sample()
False

Note

Checkpointing keeps the replay counters and ratio state together in the normal replay-buffer state dict.

add(data: Any) → int

Add a single element to the replay buffer.

Parameters:

data (Any) – data to be added to the replay buffer

Returns:

index where the data lives in the replay buffer.

append_transform(transform: Transform, *, invert: bool = False) → ReplayBuffer

Appends transform at the end.

Transforms are applied in order when sample is called.

Parameters:

transform (Transform) – The transform to be appended

Keyword Arguments:

invert (bool, optional) – if True, the transform will be inverted (forward calls will be called during writing and inverse calls during reading). Defaults to False.

Example

>>> rb = ReplayBuffer(storage=LazyMemmapStorage(10), batch_size=4)
>>> data = TensorDict({"a": torch.zeros(10)}, [10])
>>> def t(data):
...     data += 1
...     return data
>>> rb.append_transform(t, invert=True)
>>> rb.extend(data)
>>> assert (data == 1).all()

classmethod as_remote(remote_config=None)

Creates an instance of a remote ray class.

Parameters:

• cls (Python Class) – class to be remotely instantiated.

• remote_config (dict) – the quantity of CPU cores to reserve for this class. Defaults to torchrl.collectors.distributed.ray.DEFAULT_REMOTE_CLASS_CONFIG.

Returns:

A function that creates ray remote class instances.

property batch_size

The batch size of the replay buffer.

The batch size can be overridden by setting the batch_size parameter in the sample() method.

It defines both the number of samples returned by sample() and the number of samples that are yielded by the ReplayBuffer iterator.

can_sample(batch_size: int | None = None) → bool

Return whether replay readiness and ratio budget permit a sample.

client() → T

Return self for the zero-overhead direct backend.

dump(*args, **kwargs)

Alias for dumps().

dumps(path)

Saves the replay buffer on disk at the specified path.

Parameters:

path (Path or str) – path where to save the replay buffer.

Examples

>>> import tempfile
>>> import tqdm
>>> from torchrl.data import LazyMemmapStorage, TensorDictReplayBuffer
>>> from torchrl.data.replay_buffers.samplers import PrioritizedSampler, RandomSampler
>>> import torch
>>> from tensordict import TensorDict
>>> # Build and populate the replay buffer
>>> S = 1_000_000
>>> sampler = PrioritizedSampler(S, 1.1, 1.0)
>>> # sampler = RandomSampler()
>>> storage = LazyMemmapStorage(S)
>>> rb = TensorDictReplayBuffer(storage=storage, sampler=sampler)
>>>
>>> for _ in tqdm.tqdm(range(100)):
...     td = TensorDict({"obs": torch.randn(100, 3, 4), "next": {"obs": torch.randn(100, 3, 4)}, "td_error": torch.rand(100)}, [100])
...     rb.extend(td)
...     sample = rb.sample(32)
...     rb.update_tensordict_priority(sample)
>>> # save and load the buffer
>>> with tempfile.TemporaryDirectory() as tmpdir:
...     rb.dumps(tmpdir)
...
...     sampler = PrioritizedSampler(S, 1.1, 1.0)
...     # sampler = RandomSampler()
...     storage = LazyMemmapStorage(S)
...     rb_load = TensorDictReplayBuffer(storage=storage, sampler=sampler)
...     rb_load.loads(tmpdir)
...     assert len(rb) == len(rb_load)

empty(empty_write_count: bool = True)

Empties the replay buffer and reset cursor to 0.

Parameters:

empty_write_count (bool, optional) – Whether to empty the write_count attribute. Defaults to True.

extend(data: Sequence, *, update_priority: bool | None = None) → Tensor

Extends the replay buffer with one or more elements contained in an iterable.

If present, the inverse transforms will be called.`

Parameters:

data (iterable) – collection of data to be added to the replay buffer.

Keyword Arguments:

update_priority (bool, optional) – Whether to update the priority of the data. Defaults to True. Without effect in this class. See extend() for more details.

Returns:

Indices of the data added to the replay buffer.

Warning

extend() can have an ambiguous signature when dealing with lists of values, which should be interpreted either as PyTree (in which case all elements in the list will be put in a slice in the stored PyTree in the storage) or a list of values to add one at a time. To solve this, TorchRL makes the clear-cut distinction between list and tuple: a tuple will be viewed as a PyTree, a list (at the root level) will be interpreted as a stack of values to add one at a time to the buffer. For ListStorage instances, only unbound elements can be provided (no PyTrees).

property initialized: bool

Whether the replay buffer has been initialized.

insert_transform(index: int, transform: Transform, *, invert: bool = False) → ReplayBuffer

Inserts transform.

Transforms are executed in order when sample is called.

Parameters:

• index (int) – Position to insert the transform.

• transform (Transform) – The transform to be appended

Keyword Arguments:

invert (bool, optional) – if True, the transform will be inverted (forward calls will be called during writing and inverse calls during reading). Defaults to False.

property is_alive: bool

Whether this direct replay buffer remains available.

load(*args, **kwargs)

Alias for loads().

load_state_dict(state_dict: dict[str, Any]) → None

Restore replay contents, counters, and rate-limit state.

loads(path)

Loads a replay buffer state at the given path.

The buffer should have matching components and be saved using dumps().

Parameters:

path (Path or str) – path where the replay buffer was saved.

See dumps() for more info.

next()

Returns the next item in the replay buffer.

This method is used to iterate over the replay buffer in contexts where __iter__ is not available, such as RayReplayBuffer.

query(predicate: Callable[[Trajectory], bool] | None = None, *, trajectory_key: NestedKey | None = None) → list[Trajectory]

Filters the stored trajectories with a query predicate.

Splits the buffer content into trajectories (see iter_trajectories()) and returns those matching the predicate as Trajectory views.

Parameters:

predicate (Callable[[Trajectory], bool], optional) – a TrajectoryPredicate built from traj, or any callable mapping a trajectory to a boolean. Defaults to None (return all trajectories).

Keyword Arguments:

trajectory_key (NestedKey, optional) – entry holding per-transition trajectory ids. Defaults to None (auto-detection from ("collector", "traj_ids"), "traj_ids", "episode" or the done/terminated/truncated flags).

Returns:

A list of matching trajectory views, ordered chronologically (oldest trajectory first; for multi-dimensional storages, grouped by batch coordinate).

The trajectory boundaries are computed from the stored (untransformed) data with the same machinery SliceSampler uses, so samplers and queries always agree on where trajectories start and stop. This includes storages with ndim > 1 (e.g. LazyTensorStorage(..., ndim=2) holding [B, T] batches), whose trajectories are recovered per batch coordinate.

Predicates built from traj report the keys they read via required_keys(); evaluation then only fetches those entries from the storage and only runs the transforms that can affect them. Matching trajectories are extracted in full with the complete transform chain applied, so predicates and results see the same values a sampler would produce. Opaque callables are evaluated against the fully transformed content.

Note

Once the buffer has wrapped around (it is at capacity and older entries have been overwritten), the oldest trajectory may have lost its first transitions to overwriting and will appear truncated at the front. A trajectory written across the wrap point is followed through it and returned whole, in time order.

Examples

>>> from torchrl.data import traj
>>> good_trajs = rb.query((traj.reward.sum() > 100) & (traj.length >= 50))
>>> observations = good_trajs[0].observation

read_all_in_order(end: int | None = None) → Any

Read storage contents in physical order.

This is equivalent to rb[:] when end is None.

Parameters:

end (int, optional) – Number of leading storage entries to read. Defaults to the entire storage slice.

Returns:

A storage slice containing entries [:end].

register_load_hook(hook: Callable[[Any], Any])

Registers a load hook for the storage.

Note

Hooks are currently not serialized when saving a replay buffer: they must be manually re-initialized every time the buffer is created.

register_save_hook(hook: Callable[[Any], Any])

Registers a save hook for the storage.

Note

Hooks are currently not serialized when saving a replay buffer: they must be manually re-initialized every time the buffer is created.

sample(batch_size: int | None = None, return_info: bool = False, *, wait: bool = False, timeout: float | None = None, cancel_event: Any | None = None) → Any

Sample after replay readiness and ratio budget permit the request.

property sampler: Sampler

The sampler of the replay buffer.

The sampler must be an instance of Sampler.

save(*args, **kwargs)

Alias for dumps().

property service_backend: str

The canonical deployment backend for this replay buffer.

set_(key, value)

Sets the value of a key across the entire replay buffer in-place.

Parameters:

• key (NestedKey) – the key to set.

• value (torch.Tensor) – the value to write.

Returns:

self

set_at_(key, value, index)

Sets the value of a key at specified indices in the replay buffer.

Parameters:

• key (NestedKey) – the key to set.

• value (torch.Tensor) – the value to write.

• index – the indices where to write the value.

Returns:

self

set_sampler(sampler: Sampler)

Sets a new sampler in the replay buffer and returns the previous sampler.

set_storage(storage: Storage, collate_fn: Callable | None = None)

Sets a new storage in the replay buffer and returns the previous storage.

Parameters:

• storage (Storage) – the new storage for the buffer.

• collate_fn (callable, optional) – if provided, the collate_fn is set to this value. Otherwise it is reset to a default value.

set_writer(writer: Writer)

Sets a new writer in the replay buffer and returns the previous writer.

shutdown(timeout: float | None = None) → None

Wait for pending replay work and close this direct replay buffer.

Pending prefetched samples and asynchronous conditional updates are allowed to finish before their executors are closed. Any background exception is re-raised after both executors have been shut down. Repeated calls are safe.

start() → T

Return this already-started direct replay buffer.

state_dict() → dict[str, Any]

Return replay contents, counters, and rate-limit state.

stats() → dict[str, int | float | bool]

Return replay statistics with cumulative ratio state.

property storage: Storage

The storage of the replay buffer.

The storage must be an instance of Storage.

submit_update_if_present(*, index: Tensor, generation: Tensor, patch: Mapping[NestedKey, Tensor] | TensorDictBase, version_key: NestedKey | None = None, version: int | Tensor | None = None, require_newer: bool = False) → Future[ConditionalUpdateResult]

Submits an ordered conditional update on a background thread.

The update runs after all samples that were already prefetched when this method was called. Samples prefetched after this call wait for the update, while unrelated prefetch work remains parallel. Multiple submitted updates execute in submission order.

Inputs are retained by reference until the returned future completes. Callers must not mutate index, generation, patch or version in that interval. In particular, values backed by static CUDA-graph output buffers must be cloned before submission.

CUDA inputs destined for a CPU storage are copied to pinned host memory on their current stream when this method is called, and the background update waits for those copies. Work enqueued on the stream afterwards therefore does not delay the update or the samples that depend on it.

Keyword arguments have the same meaning as in update_if_present().

Returns:

A concurrent.futures.Future whose result is the ConditionalUpdateResult returned by update_if_present().

Examples

>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.data import (
...     LazyTensorStorage,
...     TensorDictReplayBuffer,
...     TensorDictRoundRobinWriter,
... )
>>> rb = TensorDictReplayBuffer(
...     storage=LazyTensorStorage(4),
...     writer=TensorDictRoundRobinWriter(track_generations=True),
... )
>>> index = rb.extend(
...     TensorDict({"value": torch.zeros(4)}, batch_size=[4])
... )
>>> generation = rb.writer.generations_of(index)
>>> future = rb.submit_update_if_present(
...     index=index,
...     generation=generation,
...     patch={"value": torch.ones(4)},
... )
>>> future.result().updated_count
4
>>> rb.shutdown()

synchronize() → None

Wait for pending samples and asynchronous updates.

Prefetched results remain queued and are returned by subsequent calls to sample() in the same order. Background exceptions are propagated to the caller.

property transform: Transform

The transform of the replay buffer.

The transform must be an instance of Transform.

update_(input_dict_or_td, clone=False, *, keys_to_update=None)

Updates the replay buffer in-place with the given dict or TensorDict.

Parameters:

• input_dict_or_td (dict or TensorDictBase) – the data to update with.

• clone (bool, optional) – whether to clone the values before writing. Defaults to False.

• keys_to_update (sequence of NestedKey, optional) – if provided, only these keys will be updated.

Returns:

self

update_if_present(*, index: Tensor, generation: Tensor, patch: Mapping[NestedKey, Tensor] | TensorDictBase, version_key: NestedKey | None = None, version: int | Tensor | None = None, require_newer: bool = False) → ConditionalUpdateResult

Conditionally updates stored records that are still live.

Replay slots are recycled by round-robin writers, so a physical index captured at sampling time can point to a different record by the time an asynchronous computation writes back. This method applies patch only to records whose (index, generation) pair still matches the writer’s current slot generation, skipping records whose slot was reused or emptied since the handle was captured. Skipped records are never modified.

The whole patch is validated (key existence, shape and dtype) before any write happens; a validation failure leaves the storage untouched. Updating a record refreshes its content, not its identity: the same handle keeps working until the slot is rewritten by add, extend or empty.

Generation tracking is opt-in: the buffer must be constructed with a writer that tracks slot generations, e.g. RoundRobinWriter(track_generations=True) (see ref_buffers_generations). Calling this method on a buffer whose writer does not track generations raises a RuntimeError.

Keyword Arguments:

• index (torch.Tensor) – storage indices, as returned by extend() or found in the sample under "index".

• generation (torch.Tensor) – slot generations captured with the indices, as found in the sample under "index_generation".

• patch (mapping of NestedKey to torch.Tensor, or TensorDictBase) – the fields to overwrite for live records. Leading dimension must match the number of records addressed by index.

• version_key (NestedKey, optional) – a stored per-record scalar field holding each record’s current version. When passed (together with version), a generation-live record is only patched if the incoming version compares favorably against the stored one, and the accepted version is written into version_key atomically with the patch. version_key may not appear in patch. Nested keys must be passed in tuple form (("nested", "version")); dotted strings are rejected. Defaults to None (no version comparison).

• version (int or torch.Tensor, optional) – the incoming version, either a scalar (broadcast to every record) or a tensor with one entry per record. Must be passed together with version_key.

• require_newer (bool, optional) – if True, a record is only patched when version > stored; if False, ties are accepted (version >= stored). When the same slot is addressed several times in one call, only the row carrying the highest incoming version is applied (the last such row on ties); the losing rows are reported in version_rejected. Defaults to False.

Returns:

A ConditionalUpdateResult whose updated mask is aligned with the input index order, with updated_count and stale_count conveniences. When version_key is passed, its version_rejected mask marks generation-live records that were rejected by the version comparison (None otherwise).

Raises:

• RuntimeError – if the storage does not support conditional updates (for example ListStorage) or the writer does not track slot generations.

• KeyError – if a patch key (or version_key) does not exist in the storage.

• ValueError – if a patch entry has an incompatible shape or dtype, if only one of version_key / version is passed, if version_key appears in patch or names a non-scalar field, or if it is a dotted string.

Examples

>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.data import (
...     LazyTensorStorage,
...     TensorDictReplayBuffer,
...     TensorDictRoundRobinWriter,
... )
>>> rb = TensorDictReplayBuffer(
...     storage=LazyTensorStorage(10),
...     writer=TensorDictRoundRobinWriter(track_generations=True),
...     batch_size=4,
... )
>>> rb.extend(TensorDict({"obs": torch.zeros(10, 3)}, batch_size=[10]))
>>> sample = rb.sample()
>>> result = rb.update_if_present(
...     index=sample["index"],
...     generation=sample["index_generation"],
...     patch={"obs": torch.ones(4, 3)},
... )
>>> print(result.updated_count, result.stale_count)
4 0

With a version comparison, outdated asynchronous writers lose deterministically:

>>> rb = TensorDictReplayBuffer(
...     storage=LazyTensorStorage(10),
...     writer=TensorDictRoundRobinWriter(track_generations=True),
...     batch_size=4,
... )
>>> rb.extend(
...     TensorDict(
...         {
...             "obs": torch.zeros(10, 3),
...             "v": torch.full((10,), 5, dtype=torch.int64),
...         },
...         batch_size=[10],
...     )
... )
>>> sample = rb.sample()
>>> result = rb.update_if_present(
...     index=sample["index"],
...     generation=sample["index_generation"],
...     patch={"obs": torch.ones(4, 3)},
...     version_key="v",
...     version=4,
...     require_newer=True,
... )
>>> print(result.updated_count, result.version_rejected_count)
0 4

wait_until_sampleable(min_items: int | None = None, timeout: float | None = None, cancel_event: Any | None = None) → bool

Waits until the replay buffer can serve a sample batch.

Parameters:

• min_items (int, optional) – requested sample batch size. Defaults to the batch size configured on the replay buffer.

• timeout (float, optional) – maximum number of seconds to wait. None waits indefinitely.

• cancel_event (optional) – event-like object exposing is_set(). The wait returns False when the event is set.

Returns:

True when the requested batch can be sampled and False after a timeout or cancellation.

Raises:

RuntimeError – if no batch size is available or the replay buffer is shut down while waiting.

Examples

>>> import threading
>>> import torch
>>> from torchrl.data import ListStorage, ReplayBuffer
>>> rb = ReplayBuffer(storage=ListStorage(4), batch_size=2)
>>> writer = threading.Thread(target=rb.extend, args=(torch.arange(2),))
>>> writer.start()
>>> rb.wait_until_sampleable(timeout=1.0)
True
>>> writer.join()

write_all(data: Any, end: int | None = None) → None

Write data back to storage in physical order.

This is equivalent to rb[:end] = data. If end is None, end defaults to data.shape[0] for tensor collections and len(data) otherwise. If data spans the full storage, this is equivalent to rb[:] = data.

Parameters:

• data – Data to write to storage.

• end (int, optional) – Number of leading storage entries to update. Defaults to data.shape[0] for tensor collections and len(data) otherwise.

property write_count: int

The total number of items written so far in the buffer through add and extend.

property writer: Writer

The writer of the replay buffer.

The writer must be an instance of Writer.