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reference/generated/torchrl.data.datasets.AtariDQNExperienceReplay

AtariDQNExperienceReplay

class torchrl.data.datasets.AtariDQNExperienceReplay(dataset_id: str, batch_size: int | None = None, *, root: str | Path | None = None, download: bool | str = True, sampler=None, writer=None, transform: 'Transform' | None = None, num_procs: int = 0, num_slices: int | None = None, slice_len: int | None = None, strict_len: bool = True, replacement: bool = True, mp_start_method: str = 'fork', **kwargs)[source]

Atari DQN Experience replay class.

The Atari DQN dataset (https://offline-rl.github.io/) is a collection of 5 training iterations of DQN over each of the Arari 2600 games for a total of 200 million frames. The sub-sampling rate (frame-skip) is equal to 4, meaning that each game dataset has 50 million steps in total.

The data format follows the TED convention. Since the dataset is quite heavy, the data formatting is done on-line, at sampling time.

To make training more modular, we split the dataset in each of the Atari games and separate each training round. Consequently, each dataset is presented as a Storage of length 50x10^6 elements. Under the hood, this dataset is split in 50 memory-mapped tensordicts of length 1 million each.

Parameters:

  • dataset_id (str) – The dataset to be downloaded. Must be part of AtariDQNExperienceReplay.available_datasets.

  • batch_size (int) – Batch-size used during sampling. Can be overridden by data.sample(batch_size) if necessary.

Keyword Arguments:

  • root (Path or str, optional) – The AtariDQN 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/atari`.

  • num_procs (int, optional) – number of processes to launch for preprocessing. Has no effect whenever the data is already downloaded. Defaults to 0 (no multiprocessing used).

  • download (bool or str, optional) – Whether the dataset should be downloaded if not found. Defaults to True. Download can also be passed as "force", in which case the downloaded data will be overwritten.

  • 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.

  • num_slices (int, optional) – the number of slices to be sampled. The batch-size must be greater or equal to the num_slices argument. Exclusive with slice_len. Defaults to None (no slice sampling). The sampler arg will override this value.

  • slice_len (int, optional) – 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. Defaults to None (no slice sampling). The sampler arg will override this value.

  • 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. Be mindful that this can result in effective batch_size shorter than the one asked for! Trajectories can be split using torchrl.collectors.split_trajectories(). Defaults to True. The sampler arg will override this value.

  • replacement (bool, optional) – if False, sampling will occur without replacement. The sampler arg will override this value.

  • mp_start_method (str, optional) – the start method for multiprocessed download. Defaults to "fork".

Variables:

  • available_datasets – list of available datasets, formatted as <game_name>/<run>. Example: “Pong/5”, “Krull/2”, …

  • dataset_id (str) – the name of the dataset.

  • episodes (torch.Tensor) – a 1d tensor indicating to what run each of the 1M frames belongs. To be used with SliceSampler to cheaply sample slices of episodes.

Examples

>>> from torchrl.data.datasets import AtariDQNExperienceReplay
>>> dataset = AtariDQNExperienceReplay("Pong/5", batch_size=128)
>>> for data in dataset:
...     print(data)
...     break
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.int32, is_shared=False),
        done: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False),
        index: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.int64, is_shared=False),
        metadata: NonTensorData(
            data={'invalid_range': MemoryMappedTensor([999998, 999999,      0,      1,      2]), 'add_count': MemoryMappedTensor(999999), 'dataset_id': 'Pong/5'}},
            batch_size=torch.Size([128]),
            device=None,
            is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False),
                observation: Tensor(shape=torch.Size([128, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
                reward: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False),
                truncated: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False)},
            batch_size=torch.Size([128]),
            device=None,
            is_shared=False),
        observation: Tensor(shape=torch.Size([128, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
        terminated: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False),
        truncated: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False)},
    batch_size=torch.Size([128]),
    device=None,
    is_shared=False)

Warning

Atari-DQN does not provide the next observation after a termination signal. In other words, there is no way to obtain the ("next", "observation") state when ("next", "done") is True. This value is filled with 0s but should not be used in practice. If TorchRL’s value estimators (ValueEstimator) are used, this should not be an issue.

Note

Because the construction of the sampler for episode sampling is slightly convoluted, we made it easy for users to pass the arguments of the SliceSampler directly to the AtariDQNExperienceReplay dataset: any of the num_slices or slice_len arguments will make the sampler an instance of SliceSampler. The strict_length can also be passed.

>>> from torchrl.data.datasets import AtariDQNExperienceReplay
>>> from torchrl.data.replay_buffers import SliceSampler
>>> dataset = AtariDQNExperienceReplay("Pong/5", batch_size=128, slice_len=64)
>>> for data in dataset:
...     print(data)
...     print(data.get("index"))  # indices are in 4 groups of consecutive values
...     break
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.int32, is_shared=False),
        done: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False),
        index: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.int64, is_shared=False),
        metadata: NonTensorData(
            data={'invalid_range': MemoryMappedTensor([999998, 999999,      0,      1,      2]), 'add_count': MemoryMappedTensor(999999), 'dataset_id': 'Pong/5'}},
            batch_size=torch.Size([128]),
            device=None,
            is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([128, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([128, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
                reward: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([128, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([128, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([128]),
            device=None,
            is_shared=False),
        observation: Tensor(shape=torch.Size([128, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
        terminated: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False),
        truncated: Tensor(shape=torch.Size([128]), device=cpu, dtype=torch.uint8, is_shared=False)},
    batch_size=torch.Size([128]),
    device=None,
    is_shared=False)
tensor([2657628, 2657629, 2657630, 2657631, 2657632, 2657633, 2657634, 2657635,
        2657636, 2657637, 2657638, 2657639, 2657640, 2657641, 2657642, 2657643,
        2657644, 2657645, 2657646, 2657647, 2657648, 2657649, 2657650, 2657651,
        2657652, 2657653, 2657654, 2657655, 2657656, 2657657, 2657658, 2657659,
        2657660, 2657661, 2657662, 2657663, 2657664, 2657665, 2657666, 2657667,
        2657668, 2657669, 2657670, 2657671, 2657672, 2657673, 2657674, 2657675,
        2657676, 2657677, 2657678, 2657679, 2657680, 2657681, 2657682, 2657683,
        2657684, 2657685, 2657686, 2657687, 2657688, 2657689, 2657690, 2657691,
        1995687, 1995688, 1995689, 1995690, 1995691, 1995692, 1995693, 1995694,
        1995695, 1995696, 1995697, 1995698, 1995699, 1995700, 1995701, 1995702,
        1995703, 1995704, 1995705, 1995706, 1995707, 1995708, 1995709, 1995710,
        1995711, 1995712, 1995713, 1995714, 1995715, 1995716, 1995717, 1995718,
        1995719, 1995720, 1995721, 1995722, 1995723, 1995724, 1995725, 1995726,
        1995727, 1995728, 1995729, 1995730, 1995731, 1995732, 1995733, 1995734,
        1995735, 1995736, 1995737, 1995738, 1995739, 1995740, 1995741, 1995742,
        1995743, 1995744, 1995745, 1995746, 1995747, 1995748, 1995749, 1995750])

Note

As always, datasets should be composed using ReplayBufferEnsemble:

>>> from torchrl.data.datasets import AtariDQNExperienceReplay
>>> from torchrl.data.replay_buffers import ReplayBufferEnsemble
>>> # we change this parameter for quick experimentation, in practice it should be left untouched
>>> AtariDQNExperienceReplay._max_runs = 2
>>> dataset_asterix = AtariDQNExperienceReplay("Asterix/5", batch_size=128, slice_len=64, num_procs=4)
>>> dataset_pong = AtariDQNExperienceReplay("Pong/5", batch_size=128, slice_len=64, num_procs=4)
>>> dataset = ReplayBufferEnsemble(dataset_pong, dataset_asterix, batch_size=128, sample_from_all=True)
>>> sample = dataset.sample()
>>> print("first sample, Asterix", sample[0])
first sample, Asterix TensorDict(
    fields={
        action: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.int32, is_shared=False),
        done: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.uint8, is_shared=False),
        index: TensorDict(
            fields={
                buffer_ids: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.int64, is_shared=False),
                index: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.int64, is_shared=False)},
            batch_size=torch.Size([64]),
            device=None,
            is_shared=False),
        metadata: NonTensorData(
            data={'invalid_range': MemoryMappedTensor([999998, 999999,      0,      1,      2]), 'add_count': MemoryMappedTensor(999999), 'dataset_id': 'Pong/5'},
            batch_size=torch.Size([64]),
            device=None,
            is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([64, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([64, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
                reward: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([64, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([64, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([64]),
            device=None,
            is_shared=False),
        observation: Tensor(shape=torch.Size([64, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
        terminated: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.uint8, is_shared=False),
        truncated: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.uint8, is_shared=False)},
    batch_size=torch.Size([64]),
    device=None,
    is_shared=False)
>>> print("second sample, Pong", sample[1])
second sample, Pong TensorDict(
    fields={
        action: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.int32, is_shared=False),
        done: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.uint8, is_shared=False),
        index: TensorDict(
            fields={
                buffer_ids: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.int64, is_shared=False),
                index: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.int64, is_shared=False)},
            batch_size=torch.Size([64]),
            device=None,
            is_shared=False),
        metadata: NonTensorData(
            data={'invalid_range': MemoryMappedTensor([999998, 999999,      0,      1,      2]), 'add_count': MemoryMappedTensor(999999), 'dataset_id': 'Asterix/5'},
            batch_size=torch.Size([64]),
            device=None,
            is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([64, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([64, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
                reward: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([64, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([64, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([64]),
            device=None,
            is_shared=False),
        observation: Tensor(shape=torch.Size([64, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
        terminated: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.uint8, is_shared=False),
        truncated: Tensor(shape=torch.Size([64]), device=cpu, dtype=torch.uint8, is_shared=False)},
    batch_size=torch.Size([64]),
    device=None,
    is_shared=False)
>>> print("Aggregate (metadata hidden)", sample)
Aggregate (metadata hidden) LazyStackedTensorDict(
    fields={
        action: Tensor(shape=torch.Size([2, 64]), device=cpu, dtype=torch.int32, is_shared=False),
        done: Tensor(shape=torch.Size([2, 64]), device=cpu, dtype=torch.uint8, is_shared=False),
        index: LazyStackedTensorDict(
            fields={
                buffer_ids: Tensor(shape=torch.Size([2, 64]), device=cpu, dtype=torch.int64, is_shared=False),
                index: Tensor(shape=torch.Size([2, 64]), device=cpu, dtype=torch.int64, is_shared=False)},
            exclusive_fields={
            },
            batch_size=torch.Size([2, 64]),
            device=None,
            is_shared=False,
            stack_dim=0),
        metadata: LazyStackedTensorDict(
            fields={
            },
            exclusive_fields={
            },
            batch_size=torch.Size([2, 64]),
            device=None,
            is_shared=False,
            stack_dim=0),
        next: LazyStackedTensorDict(
            fields={
                done: Tensor(shape=torch.Size([2, 64, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([2, 64, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
                reward: Tensor(shape=torch.Size([2, 64]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([2, 64, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([2, 64, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            exclusive_fields={
            },
            batch_size=torch.Size([2, 64]),
            device=None,
            is_shared=False,
            stack_dim=0),
        observation: Tensor(shape=torch.Size([2, 64, 84, 84]), device=cpu, dtype=torch.uint8, is_shared=False),
        terminated: Tensor(shape=torch.Size([2, 64]), device=cpu, dtype=torch.uint8, is_shared=False),
        truncated: Tensor(shape=torch.Size([2, 64]), device=cpu, dtype=torch.uint8, is_shared=False)},
    exclusive_fields={
    },
    batch_size=torch.Size([2, 64]),
    device=None,
    is_shared=False,
    stack_dim=0)
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()
abstract property data_path: Path

Path to the dataset, including split.

abstract property data_path_root: Path

Path to the dataset root.

delete()

Deletes a dataset storage from disk.

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.

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, dest: str | Path, num_frames: int | None = None)[source]

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>)
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.

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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