PrioritizedSliceSampler

class torchrl.data.replay_buffers.PrioritizedSliceSampler(max_capacity: int, alpha: float, beta: float, eps: float = 1e-08, dtype: torch.dtype = torch.float32, reduction: str = 'max', *, num_slices: int | None = None, slice_len: int | None = None, end_key: NestedKey | None = None, traj_key: NestedKey | None = None, ends: torch.Tensor | None = None, trajectories: torch.Tensor | None = None, cache_values: bool = False, truncated_key: NestedKey | None = ('next', 'truncated'), strict_length: bool = True, compile: bool | dict = False, span: bool | int | tuple[bool | int, bool | int] = False, max_priority_within_buffer: bool = False)

Samples slices of data along the first dimension, given start and stop signals, using prioritized sampling.

This class combines trajectory sampling with Prioritized Experience Replay (PER) as presented in “Schaul, T.; Quan, J.; Antonoglou, I.; and Silver, D. 2015. Prioritized experience replay.” (https://arxiv.org/abs/1511.05952)

Core Idea: Instead of sampling trajectory slices uniformly, this sampler prioritizes trajectory start points based on the importance of the transitions at those positions. This allows focusing learning on the most informative parts of trajectories.

How it works: 1. Each transition is assigned a priority based on its TD error: \(p_i = |\\delta_i| + \\epsilon\) 2. Trajectory start points are sampled with probability: \(P(i) = \frac{p_i^\alpha}{\\sum_j p_j^\alpha}\) 3. Importance sampling weights correct for bias: \(w_i = (N \\cdot P(i))^{-\beta}\) 4. Complete trajectory slices are extracted from the sampled start points

For more info see SliceSampler and PrioritizedSampler.

Warning

PrioritizedSliceSampler will look at the priorities of the individual transitions and sample the start points accordingly. This means that transitions with a low priority may as well appear in the samples if they follow another of higher priority, and transitions with a high priority but closer to the end of a trajectory may never be sampled if they cannot be used as start points. Currently, it is the user responsibility to aggregate priorities across items of a trajectory using update_priority().

Parameters:

• max_capacity (int) – maximum capacity of the buffer.

• alpha (float) – exponent \(\alpha\) determines how much prioritization is used. - \(\alpha = 0\): uniform sampling of trajectory start points - \(\alpha = 1\): full prioritization based on TD error magnitude at start points - Typical values: 0.4-0.7 for balanced prioritization - Higher \(\alpha\) means more aggressive prioritization of high-error trajectory regions

• beta (float) – importance sampling negative exponent \(\beta\). - \(\beta\) controls the correction for the bias introduced by prioritization - \(\beta = 0\): no correction (biased towards high-priority trajectory regions) - \(\beta = 1\): full correction (unbiased but potentially unstable) - Typical values: start at 0.4-0.6 and anneal to 1.0 during training - Lower \(\beta\) early in training provides stability, higher \(\beta\) later reduces bias

• eps (float, optional) – small constant added to priorities to ensure no transition has zero priority. This prevents trajectory regions from never being sampled. Defaults to 1e-8.

• reduction (str, optional) – the reduction method for multidimensional tensordicts (i.e., stored trajectory). Can be one of “max”, “min”, “median” or “mean”.

Parameter Guidelines: - :math:`alpha` (alpha): Controls how much to prioritize high-error trajectory regions

• 0.4-0.7: Good balance between learning speed and stability

• 1.0: Maximum prioritization (may be unstable)

• 0.0: Uniform sampling (no prioritization benefit)

• :math:`beta` (beta): Controls importance sampling correction

  • Start at 0.4-0.6 for training stability

  • Anneal to 1.0 over training to reduce bias

  • Lower values = more stable but biased

  • Higher values = less biased but potentially unstable

• :math:`\epsilon`: Small constant to prevent zero priorities

  • 1e-8: Good default value

  • Too small: may cause numerical issues

  • Too large: reduces prioritization effect

Keyword Arguments:

• num_slices (int) – the number of slices to be sampled. The batch-size must be greater or equal to the num_slices argument. Exclusive with slice_len.

• slice_len (int) – the length of the slices to be sampled. The batch-size must be greater or equal to the slice_len argument and divisible by it. Exclusive with num_slices.

• end_key (NestedKey, optional) – the key indicating the end of a trajectory (or episode). Defaults to ("next", "done").

• traj_key (NestedKey, optional) – the key indicating the trajectories. Defaults to "episode" (commonly used across datasets in TorchRL).

• ends (torch.Tensor, optional) – a 1d boolean tensor containing the end of run signals. To be used whenever the end_key or traj_key is expensive to get, or when this signal is readily available. Must be used with cache_values=True and cannot be used in conjunction with end_key or traj_key.

• trajectories (torch.Tensor, optional) – a 1d integer tensor containing the run ids. To be used whenever the end_key or traj_key is expensive to get, or when this signal is readily available. Must be used with cache_values=True and cannot be used in conjunction with end_key or traj_key.

• cache_values (bool, optional) –

  to be used with static datasets. Will cache the start and end signal of the trajectory. This can be safely used even if the trajectory indices change during calls to extend as this operation will erase the cache.

  Warning

  cache_values=True will not work if the sampler is used with a storage that is extended by another buffer. For instance:

  >>> buffer0 = ReplayBuffer(storage=storage,
  ...     sampler=SliceSampler(num_slices=8, cache_values=True),
  ...     writer=ImmutableWriter())
  >>> buffer1 = ReplayBuffer(storage=storage,
  ...     sampler=other_sampler)
  >>> # Wrong! Does not erase the buffer from the sampler of buffer0
  >>> buffer1.extend(data)
  

  Warning

  cache_values=True will not work as expected if the buffer is shared between processes and one process is responsible for writing and one process for sampling, as erasing the cache can only be done locally.

• truncated_key (NestedKey, optional) – If not None, this argument indicates where a truncated signal should be written in the output data. This is used to indicate to value estimators where the provided trajectory breaks. Defaults to ("next", "truncated"). This feature only works with TensorDictReplayBuffer instances (otherwise the truncated key is returned in the info dictionary returned by the sample() method).

• strict_length (bool, optional) – if False, trajectories of length shorter than slice_len (or batch_size // num_slices) will be allowed to appear in the batch. If True, trajectories shorted than required will be filtered out. Be mindful that this can result in effective batch_size shorter than the one asked for! Trajectories can be split using split_trajectories(). Defaults to True.

• compile (bool or dict of kwargs, optional) – if True, the bottleneck of the sample() method will be compiled with compile(). Keyword arguments can also be passed to torch.compile with this arg. Defaults to False.

• span (bool, int, Tuple[bool | int, bool | int], optional) – if provided, the sampled trajectory will span across the left and/or the right. This means that possibly fewer elements will be provided than what was required. A boolean value means that at least one element will be sampled per trajectory. An integer i means that at least slice_len - i samples will be gathered for each sampled trajectory. Using tuples allows a fine grained control over the span on the left (beginning of the stored trajectory) and on the right (end of the stored trajectory).

• max_priority_within_buffer (bool, optional) – if True, the max-priority is tracked within the buffer. When False, the max-priority tracks the maximum value since the instantiation of the sampler. Defaults to False.

Examples

>>> import torch
>>> from torchrl.data.replay_buffers import TensorDictReplayBuffer, LazyMemmapStorage, PrioritizedSliceSampler
>>> from tensordict import TensorDict
>>> sampler = PrioritizedSliceSampler(max_capacity=9, num_slices=3, alpha=0.7, beta=0.9)
>>> rb = TensorDictReplayBuffer(storage=LazyMemmapStorage(9), sampler=sampler, batch_size=6)
>>> data = TensorDict(
...     {
...         "observation": torch.randn(9,16),
...         "action": torch.randn(9, 1),
...         "episode": torch.tensor([0,0,0,1,1,1,2,2,2], dtype=torch.long),
...         "steps": torch.tensor([0,1,2,0,1,2,0,1,2], dtype=torch.long),
...         ("next", "observation"): torch.randn(9,16),
...         ("next", "reward"): torch.randn(9,1),
...         ("next", "done"): torch.tensor([0,0,1,0,0,1,0,0,1], dtype=torch.bool).unsqueeze(1),
...     },
...     batch_size=[9],
... )
>>> rb.extend(data)
>>> sample, info = rb.sample(return_info=True)
>>> print("episode", sample["episode"].tolist())
episode [2, 2, 2, 2, 1, 1]
>>> print("steps", sample["steps"].tolist())
steps [1, 2, 0, 1, 1, 2]
>>> print("weight", info["_weight"].tolist())
weight [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
>>> priority = torch.tensor([0,3,3,0,0,0,1,1,1])
>>> rb.update_priority(torch.arange(0,9,1), priority=priority)
>>> sample, info = rb.sample(return_info=True)
>>> print("episode", sample["episode"].tolist())
episode [2, 2, 2, 2, 2, 2]
>>> print("steps", sample["steps"].tolist())
steps [1, 2, 0, 1, 0, 1]
>>> print("weight", info["_weight"].tolist())
weight [9.120110917137936e-06, 9.120110917137936e-06, 9.120110917137936e-06, 9.120110917137936e-06, 9.120110917137936e-06, 9.120110917137936e-06]

update_priority(index: int | torch.Tensor, priority: float | torch.Tensor, *, storage: TensorStorage | None = None) → None

Updates the priority of the data pointed by the index.

Parameters:

• index (int or torch.Tensor) – indexes of the priorities to be updated.

• priority (Number or torch.Tensor) – new priorities of the indexed elements.

Keyword Arguments:

storage (Storage, optional) – a storage used to map the Nd index size to the 1d size of the sum_tree and min_tree. Only required whenever index.ndim > 2.