reference/generated/torchrl.data.replay_buffers.ImmutableDatasetWriter

Run in Google Colab

Colab

Download Notebook

Notebook

View on GitHub

GitHub

ImmutableDatasetWriter

class torchrl.data.replay_buffers.ImmutableDatasetWriter(compilable: bool = False)

A blocking writer for immutable datasets.

add(data: Any) → int

Inserts one piece of data at an appropriate index, and returns that index.

extend(data: Sequence) → Tensor

Inserts a series of data points at appropriate indices, and returns a tensor containing the indices.

generations_of(index: int | Tensor) → Tensor

Returns the generation stamp for each physical slot in index.

A slot’s stamp advances once per write it receives, so a single extend that wraps the storage advances a reused slot once per write. Comparing a stamp captured at sampling time against the current stamp tells you whether the slot still holds the data you sampled.

Writers that do not track slot reuse – and writers constructed with track_generations=False, which is the default – report -1 everywhere. Never-written slots also report -1, so -1 means “no usable stamp” rather than “generation zero”.

Parameters:

index (int or torch.Tensor) – dim-0 slot indices. A 1-D tensor is always read as a batch of slot indices; for a storage with ndim > 1, pass a tuple of per-dimension indices (as extend() returns) to identify a single cell – only its dim-0 component is used, since a generation stamps a whole dim-0 slot.

Returns:

int64 stamps shaped like the dim-0 component of index, on index’s device.

Return type:

torch.Tensor

tracks_generations: bool = False

Whether this writer stamps storage slots with a reuse generation. Always False unless the writer both supports generation tracking and was constructed with it enabled (see RoundRobinWriter).