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D4RLExperienceReplay

class torchrl.data.datasets.D4RLExperienceReplay(dataset_id, batch_size: int, sampler: Sampler | None = None, writer: Writer | None = None, collate_fn: Callable | None = None, pin_memory: bool = False, prefetch: int | None = None, transform: 'torchrl.envs.Transform' | None = None, split_trajs: bool = False, from_env: bool = False, use_truncated_as_done: bool = True, direct_download: bool = None, terminate_on_end: bool = None, download: bool = True, root: str | Path | None = None, **env_kwargs)[source]

An Experience replay class for D4RL.

To install D4RL, follow the instructions on the official repo.

The data format follows the TED convention. The replay buffer contains the env specs under D4RLExperienceReplay.specs.

If present, metadata will be written in D4RLExperienceReplay.metadata and excluded from the dataset.

The transitions are reconstructed using done = terminated | truncated and the ("next", "observation") of "done" states are zeroed.

Parameters:

  • dataset_id (str) – the dataset_id of the D4RL env to get the data from.

  • batch_size (int) – the batch size to use during sampling.

  • sampler (Sampler, optional) – the sampler to be used. If none is provided a default RandomSampler() will be used.

  • writer (Writer, optional) – the writer to be used. If none is provided a default ImmutableDatasetWriter will be used.

  • collate_fn (callable, optional) – merges a list of samples to form a mini-batch of Tensor(s)/outputs. Used when using batched loading from a map-style dataset.

  • pin_memory (bool) – whether pin_memory() should be called on the rb samples.

  • prefetch (int, optional) – number of next batches to be prefetched using multithreading.

  • transform (Transform, optional) – Transform to be executed when sample() is called. To chain transforms use the Compose class.

  • split_trajs (bool, optional) – if True, the trajectories will be split along the first dimension and padded to have a matching shape. To split the trajectories, the "done" signal will be used, which is recovered via done = truncated | terminated. In other words, it is assumed that any truncated or terminated signal is equivalent to the end of a trajectory. For some datasets from D4RL, this may not be true. It is up to the user to make accurate choices regarding this usage of split_trajs. Defaults to False.

  • from_env (bool, optional) –

    if True, env.get_dataset() will be used to retrieve the dataset. Otherwise d4rl.qlearning_dataset() will be used. Defaults to True.

    Note

    Using from_env=False will provide fewer data than from_env=True. For instance, the info keys will be left out. Usually, from_env=False with terminate_on_end=True will lead to the same result as from_env=True, with the latter containing meta-data and info entries that the former does not possess.

    Note

    The keys in from_env=True and from_env=False may unexpectedly differ. In particular, the "truncated" key (used to determine the end of an episode) may be absent when from_env=False but present otherwise, leading to a different slicing when traj_splits is enabled.

  • direct_download (bool) – if True, the data will be downloaded without requiring D4RL. If None, if d4rl is present in the env it will be used to download the dataset, otherwise the download will fall back on direct_download=True. This is not compatible with from_env=True. Defaults to None.

  • use_truncated_as_done (bool, optional) – if True, done = terminated | truncated. Otherwise, only the terminated key is used. Defaults to True.

  • terminate_on_end (bool, optional) – Set done=True on the last timestep in a trajectory. Default is False, and will discard the last timestep in each trajectory. This is to be used only with direct_download=False.

  • root (Path or str, optional) – The D4RL dataset root directory. The actual dataset memory-mapped files will be saved under <root>/<dataset_id>. If none is provided, it defaults to ``~/.cache/torchrl/d4rl`.

  • download (bool, optional) – Whether the dataset should be downloaded if not found. Defaults to True.

  • **env_kwargs (key-value pairs) – additional kwargs for d4rl.qlearning_dataset().

Examples

>>> from torchrl.data.datasets.d4rl import D4RLExperienceReplay
>>> from torchrl.envs import ObservationNorm
>>> data = D4RLExperienceReplay("maze2d-umaze-v1", 128)
>>> # we can append transforms to the dataset
>>> data.append_transform(ObservationNorm(loc=-1, scale=1.0, in_keys=["observation"]))
>>> data.sample(128)
add(data: TensorDictBase) → 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()
property data_path: Path

Path to the dataset, including split.

property data_path_root: Path

Path to the dataset root.

delete()

Deletes a dataset storage from disk.

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()

Empties the replay buffer and reset cursor to 0.

extend(tensordicts: TensorDictBase) → 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.

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).

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.

load(*args, **kwargs)

Alias for loads().

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.

preprocess(fn: Callable[[TensorDictBase], TensorDictBase], dim: int = 0, num_workers: int | None = None, *, chunksize: int | None = None, num_chunks: int | None = None, pool: mp.Pool | None = None, generator: torch.Generator | None = None, max_tasks_per_child: int | None = None, worker_threads: int = 1, index_with_generator: bool = False, pbar: bool = False, mp_start_method: str | None = None, num_frames: int | None = None, dest: str | Path) → TensorStorage

Preprocesses a dataset and returns a new storage with the formatted data.

The data transform must be unitary (work on a single sample of the dataset).

Args and Keyword Args are forwarded to map().

The dataset can subsequently be deleted using delete().

Keyword Arguments:

  • dest (path or equivalent) – a path to the location of the new dataset.

  • num_frames (int, optional) – if provided, only the first num_frames will be transformed. This is useful to debug the transform at first.

Returns: A new storage to be used within a ReplayBuffer instance.

Examples

>>> from torchrl.data.datasets import MinariExperienceReplay
>>>
>>> data = MinariExperienceReplay(
...     list(MinariExperienceReplay.available_datasets)[0],
...     batch_size=32
...     )
>>> print(data)
MinariExperienceReplay(
    storages=TensorStorage(TensorDict(
        fields={
            action: MemoryMappedTensor(shape=torch.Size([1000000, 8]), device=cpu, dtype=torch.float32, is_shared=True),
            episode: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.int64, is_shared=True),
            info: TensorDict(
                fields={
                    distance_from_origin: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                    forward_reward: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                    goal: MemoryMappedTensor(shape=torch.Size([1000000, 2]), device=cpu, dtype=torch.float64, is_shared=True),
                    qpos: MemoryMappedTensor(shape=torch.Size([1000000, 15]), device=cpu, dtype=torch.float64, is_shared=True),
                    qvel: MemoryMappedTensor(shape=torch.Size([1000000, 14]), device=cpu, dtype=torch.float64, is_shared=True),
                    reward_ctrl: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                    reward_forward: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                    reward_survive: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                    success: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.bool, is_shared=True),
                    x_position: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                    x_velocity: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                    y_position: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                    y_velocity: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True)},
                batch_size=torch.Size([1000000]),
                device=cpu,
                is_shared=False),
            next: TensorDict(
                fields={
                    done: MemoryMappedTensor(shape=torch.Size([1000000, 1]), device=cpu, dtype=torch.bool, is_shared=True),
                    info: TensorDict(
                        fields={
                            distance_from_origin: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                            forward_reward: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                            goal: MemoryMappedTensor(shape=torch.Size([1000000, 2]), device=cpu, dtype=torch.float64, is_shared=True),
                            qpos: MemoryMappedTensor(shape=torch.Size([1000000, 15]), device=cpu, dtype=torch.float64, is_shared=True),
                            qvel: MemoryMappedTensor(shape=torch.Size([1000000, 14]), device=cpu, dtype=torch.float64, is_shared=True),
                            reward_ctrl: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                            reward_forward: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                            reward_survive: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                            success: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.bool, is_shared=True),
                            x_position: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                            x_velocity: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                            y_position: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True),
                            y_velocity: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.float64, is_shared=True)},
                        batch_size=torch.Size([1000000]),
                        device=cpu,
                        is_shared=False),
                    observation: TensorDict(
                        fields={
                            achieved_goal: MemoryMappedTensor(shape=torch.Size([1000000, 2]), device=cpu, dtype=torch.float64, is_shared=True),
                            desired_goal: MemoryMappedTensor(shape=torch.Size([1000000, 2]), device=cpu, dtype=torch.float64, is_shared=True),
                            observation: MemoryMappedTensor(shape=torch.Size([1000000, 27]), device=cpu, dtype=torch.float64, is_shared=True)},
                        batch_size=torch.Size([1000000]),
                        device=cpu,
                        is_shared=False),
                    reward: MemoryMappedTensor(shape=torch.Size([1000000, 1]), device=cpu, dtype=torch.float64, is_shared=True),
                    terminated: MemoryMappedTensor(shape=torch.Size([1000000, 1]), device=cpu, dtype=torch.bool, is_shared=True),
                    truncated: MemoryMappedTensor(shape=torch.Size([1000000, 1]), device=cpu, dtype=torch.bool, is_shared=True)},
                batch_size=torch.Size([1000000]),
                device=cpu,
                is_shared=False),
            observation: TensorDict(
                fields={
                    achieved_goal: MemoryMappedTensor(shape=torch.Size([1000000, 2]), device=cpu, dtype=torch.float64, is_shared=True),
                    desired_goal: MemoryMappedTensor(shape=torch.Size([1000000, 2]), device=cpu, dtype=torch.float64, is_shared=True),
                    observation: MemoryMappedTensor(shape=torch.Size([1000000, 27]), device=cpu, dtype=torch.float64, is_shared=True)},
                batch_size=torch.Size([1000000]),
                device=cpu,
                is_shared=False)},
        batch_size=torch.Size([1000000]),
        device=cpu,
        is_shared=False)),
    samplers=RandomSampler,
    writers=ImmutableDatasetWriter(),
batch_size=32,
transform=Compose(
),
collate_fn=<function _collate_id at 0x120e21dc0>)
>>> from torchrl.envs import CatTensors, Compose
>>> from tempfile import TemporaryDirectory
>>>
>>> cat_tensors = CatTensors(
...     in_keys=[("observation", "observation"), ("observation", "achieved_goal"),
...              ("observation", "desired_goal")],
...     out_key="obs"
...     )
>>> cat_next_tensors = CatTensors(
...     in_keys=[("next", "observation", "observation"),
...              ("next", "observation", "achieved_goal"),
...              ("next", "observation", "desired_goal")],
...     out_key=("next", "obs")
...     )
>>> t = Compose(cat_tensors, cat_next_tensors)
>>>
>>> def func(td):
...     td = td.select(
...         "action",
...         "episode",
...         ("next", "done"),
...         ("next", "observation"),
...         ("next", "reward"),
...         ("next", "terminated"),
...         ("next", "truncated"),
...         "observation"
...         )
...     td = t(td)
...     return td
>>> with TemporaryDirectory() as tmpdir:
...     new_storage = data.preprocess(func, num_workers=4, pbar=True, mp_start_method="fork", dest=tmpdir)
...     rb = ReplayBuffer(storage=new_storage)
...     print(rb)
ReplayBuffer(
    storage=TensorStorage(
        data=TensorDict(
            fields={
                action: MemoryMappedTensor(shape=torch.Size([1000000, 8]), device=cpu, dtype=torch.float32, is_shared=True),
                episode: MemoryMappedTensor(shape=torch.Size([1000000]), device=cpu, dtype=torch.int64, is_shared=True),
                next: TensorDict(
                    fields={
                        done: MemoryMappedTensor(shape=torch.Size([1000000, 1]), device=cpu, dtype=torch.bool, is_shared=True),
                        obs: MemoryMappedTensor(shape=torch.Size([1000000, 31]), device=cpu, dtype=torch.float64, is_shared=True),
                        observation: TensorDict(
                            fields={
                            },
                            batch_size=torch.Size([1000000]),
                            device=cpu,
                            is_shared=False),
                        reward: MemoryMappedTensor(shape=torch.Size([1000000, 1]), device=cpu, dtype=torch.float64, is_shared=True),
                        terminated: MemoryMappedTensor(shape=torch.Size([1000000, 1]), device=cpu, dtype=torch.bool, is_shared=True),
                        truncated: MemoryMappedTensor(shape=torch.Size([1000000, 1]), device=cpu, dtype=torch.bool, is_shared=True)},
                    batch_size=torch.Size([1000000]),
                    device=cpu,
                    is_shared=False),
                obs: MemoryMappedTensor(shape=torch.Size([1000000, 31]), device=cpu, dtype=torch.float64, is_shared=True),
                observation: TensorDict(
                    fields={
                    },
                    batch_size=torch.Size([1000000]),
                    device=cpu,
                    is_shared=False)},
            batch_size=torch.Size([1000000]),
            device=cpu,
            is_shared=False),
        shape=torch.Size([1000000]),
        len=1000000,
        max_size=1000000),
    sampler=RandomSampler(),
    writer=RoundRobinWriter(cursor=0, full_storage=True),
    batch_size=None,
    collate_fn=<function _collate_id at 0x168406fc0>)
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, include_info: bool = None) → TensorDictBase

Samples a batch of data from the replay buffer.

Uses Sampler to sample indices, and retrieves them from Storage.

Parameters:

  • batch_size (int, optional) – size of data to be collected. If none is provided, this method will sample a batch-size as indicated by the sampler.

  • return_info (bool) – whether to return info. If True, the result is a tuple (data, info). If False, the result is the data.

Returns:

A tensordict containing a batch of data selected in the replay buffer. A tuple containing this tensordict and info if return_info flag is set to True.

property sampler

The sampler of the replay buffer.

The sampler must be an instance of Sampler.

save(*args, **kwargs)

Alias for dumps().

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.

property storage

The storage of the replay buffer.

The storage must be an instance of Storage.

property writer

The writer of the replay buffer.

The writer must be an instance of Writer.

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