reference/generated/torchrl.data.replay_buffers.SamplerEnsemble

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SamplerEnsemble

class torchrl.data.replay_buffers.SamplerEnsemble(*args, **kwargs)

An ensemble of samplers.

This class is designed to work with ReplayBufferEnsemble. It contains the samplers as well as the sampling strategy hyperparameters.

Parameters:

samplers (sequence of Sampler) – the samplers to make the composite sampler.

Keyword Arguments:

• p (list, tensor of probabilities, or "sampleable", optional) – if provided, indicates the weights of each dataset during sampling. "sampleable" recomputes weights from the number of records or valid slice windows currently available in each member and excludes members that cannot provide a batch.

• sample_from_all (bool, optional) – if True, each dataset will be sampled from. This is not compatible with the p argument. Defaults to False.

• num_buffer_sampled (int, optional) – the number of buffers to sample. if sample_from_all=True, this has no effect, as it defaults to the number of buffers. If sample_from_all=False, buffers will be sampled according to the probabilities p.

Warning

The indices provided in the info dictionary are placed in a TensorDict with keys index and buffer_ids that allow the upper ReplayBufferEnsemble and StorageEnsemble objects to retrieve the data. This format is different from with other samplers which usually return indices as regular tensors.

can_sample(storage: StorageEnsemble, batch_size: int) → bool

Returns whether the selected ensemble strategy can serve a batch.

property requires_shared_state: bool

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.