RobosetExperienceReplay¶

class torchrl.data.datasets.RobosetExperienceReplay(dataset_id, batch_size: int, *, root: str | Path | None = None, download: bool = True, 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, **env_kwargs)[source]¶

Roboset 经验回放数据集。

此类从 roboset 下载 H5 数据并在 mmap 格式中进行处理，这使得索引（因此采样）更快。

在此处了解有关 roboset 的更多信息：https://sites.google.com/view/robohive/roboset

数据格式遵循 TED 约定。

参数:

dataset_id (str) – 要下载的数据集。必须是 RobosetExperienceReplay.available_datasets 的一部分。
batch_size (int) – 采样期间使用的批次大小。如果需要，可以由 data.sample(batch_size) 覆盖。

关键字参数:

root (Path 或 str, 可选) – Roboset 数据集根目录。实际数据集内存映射文件将保存在 <root>/<dataset_id> 下。如果未提供，则默认为 ``~/.cache/torchrl/roboset`。
download (bool 或 str, 可选) – 如果未找到，是否应下载数据集。默认为 True。下载也可以作为 "force" 传递，在这种情况下，下载的数据将被覆盖。
sampler (Sampler, 可选) – 要使用的采样器。如果未提供，将使用默认的 RandomSampler()。
writer (Writer, 可选) – 要使用的写入器。如果未提供，将使用默认的 ImmutableDatasetWriter。
collate_fn (callable, 可选) – 将样本列表合并为一个张量 (s) / 输出的小批量。在从地图样式数据集进行批处理加载时使用。
pin_memory (bool) – 是否应在 rb 样本上调用 pin_memory()。
prefetch (int, 可选) – 使用多线程预取的下一个批次的数目。
transform (Transform, 可选) – 在调用 sample() 时要执行的转换。要链接转换，请使用 Compose 类。
split_trajs (bool, 可选) – 如果为 True，则轨迹将沿第一个维度拆分并填充以具有匹配的形状。要拆分轨迹，将使用 "done" 信号，该信号通过 done = truncated | terminated 恢复。换句话说，假设任何 truncated 或 terminated 信号都等效于轨迹的结束。默认为 False。

变量:

available_datasets – 要下载的接受项列表。

示例

>>> import torch
>>> torch.manual_seed(0)
>>> from torchrl.envs.transforms import ExcludeTransform
>>> from torchrl.data.datasets import RobosetExperienceReplay
>>> d = RobosetExperienceReplay("FK1-v4(expert)/FK1_MicroOpenRandom_v2d-v4", batch_size=32,
...     transform=ExcludeTransform("info", ("next", "info")))  # excluding info dict for conciseness
>>> for batch in d:
...     break
>>> # data is organised by seed and episode, but stored contiguously
>>> print(f"{batch['seed']}, {batch['episode']}")
tensor([2, 1, 0, 0, 1, 1, 0, 0, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 0, 2, 0, 2, 2, 1,
        0, 2, 0, 0, 1, 1, 2, 1]) tensor([17, 20, 18,  9,  6,  1, 12,  6,  2,  6,  8, 15,  8, 21, 17,  3,  9, 20,
        23, 12,  3, 16, 19, 16, 16,  4,  4, 12,  1,  2, 15, 24])
>>> print(batch)
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([32, 9]), device=cpu, dtype=torch.float64, is_shared=False),
        done: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        episode: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.int64, is_shared=False),
        index: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.int64, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([32, 75]), device=cpu, dtype=torch.float64, is_shared=False),
                reward: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.float64, is_shared=False),
                terminated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([32]),
            device=cpu,
            is_shared=False),
        observation: Tensor(shape=torch.Size([32, 75]), device=cpu, dtype=torch.float64, is_shared=False),
        seed: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.int64, is_shared=False),
        terminated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        time: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.float64, is_shared=False)},
        truncated: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([32]),
    device=cpu,
    is_shared=False)

add(data: TensorDictBase) → int¶

将单个元素添加到回放缓冲区。

参数:: data (Any) – 要添加到回放缓冲区的数据
返回值:: 数据在回放缓冲区中的位置索引。

append_transform(transform: Transform, *, invert: bool = False) → ReplayBuffer¶

在末尾追加变换。

当调用 sample 时，变换按顺序应用。

参数:: transform (Transform) – 要追加的变换
关键字参数:: invert (bool, optional) – 如果为 True，则变换将被反转（向前调用将在写入期间调用，反向调用将在读取期间调用）。默认值为 False。

示例

>>> 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¶: 数据集的路径，包括分割。

property data_path_root¶: 数据集根目录的路径。

delete()¶: 从磁盘删除数据集存储。

dump(*args, **kwargs)¶: dumps() 的别名。

dumps(path)¶

将重放缓冲区保存到指定路径的磁盘上。

参数:: path (Path or str) – 保存重放缓冲区的路径。

示例

>>> 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()¶: 清空重放缓冲区并将光标重置为 0。

extend(tensordicts: TensorDictBase) → Tensor¶

使用可迭代对象中包含的一个或多个元素扩展重放缓冲区。

如果存在，将调用反向变换。`

参数:: data (iterable) – 要添加到重放缓冲区的数据集合。
返回值:: 添加到重放缓冲区的数据的索引。

警告

extend() 在处理值列表时可能具有模棱两可的签名，这些值应该被解释为 PyTree（在这种情况下，列表中的所有元素都将被放入存储的 PyTree 中的切片中）或要逐个添加的值列表。为了解决这个问题，TorchRL 对列表和元组做出了明确的区别：元组将被视为 PyTree，列表（在根级别）将被解释为要逐个添加到缓冲区的堆叠值。对于 ListStorage 实例，只能提供未绑定的元素（没有 PyTrees）。

insert_transform(index: int, transform: Transform, *, invert: bool = False) → ReplayBuffer¶

插入变换。

当调用 sample 时，变换按顺序执行。

参数:

index (int) – 插入变换的位置。
transform (Transform) – 要追加的变换

关键字参数:

invert (bool, optional) – 如果为 True，则变换将被反转（向前调用将在写入期间调用，反向调用将在读取期间调用）。默认值为 False。

load(*args, **kwargs)¶: loads() 的别名。

loads(path)¶

在给定路径加载重放缓冲区状态。

缓冲区应具有匹配的组件，并使用 dumps() 保存。

参数:: path (Path or str) – 保存重放缓冲区的路径。

有关更多信息，请参见 dumps()。

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¶

预处理数据集并返回包含格式化数据的新的存储。

数据变换必须是单一的（在数据集的单个样本上工作）。

参数和关键字参数被转发到 map()。

数据集随后可以使用 delete() 删除。

关键字参数:

dest (路径或 等效项) – 新数据集位置的路径。
num_frames (int, 可选) – 如果提供，将仅转换前 num_frames 帧。这对于在调试转换时很有用。

返回：一个新的存储器，将在 ReplayBuffer 实例中使用。

示例

>>> 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])¶: 为存储注册加载钩子。

注意

在保存重播缓冲区时，当前不会序列化钩子：每次创建缓冲区时，都必须手动重新初始化它们。

register_save_hook(hook: Callable[[Any], Any])¶: 为存储注册保存钩子。

注意

在保存重播缓冲区时，当前不会序列化钩子：每次创建缓冲区时，都必须手动重新初始化它们。

sample(batch_size: int | None = None, return_info: bool = False, include_info: bool = None) → TensorDictBase¶

从重播缓冲区中采样一批数据。

使用 Sampler 采样索引，并从 Storage 中检索它们。

参数:

batch_size (int, 可选) – 要收集的数据的大小。如果没有提供，此方法将根据采样器指示的批次大小采样。
return_info (bool) – 是否返回信息。如果为 True，则结果为元组 (data, info)。如果为 False，则结果为数据。

返回值:

一个包含在重播缓冲区中选择的批次数据的 tensordict。如果 return_info 标志设置为 True，则包含此 tensordict 和信息的元组。

property sampler¶

重播缓冲区的采样器。

采样器必须是 Sampler 的实例。

save(*args, **kwargs)¶: dumps() 的别名。

set_sampler(sampler: Sampler)¶: 在重播缓冲区中设置新的采样器，并返回之前的采样器。

set_storage(storage: Storage, collate_fn: Callable | None = None)¶

在重播缓冲区中设置新的存储，并返回之前的存储。

参数:

storage (Storage) – 缓冲区的新的存储。
collate_fn (可调用, 可选) – 如果提供，collate_fn 将设置为该值。否则它将重置为默认值。

set_writer(writer: Writer)¶: 在重播缓冲区中设置新的写入器，并返回之前的写入器。

property storage¶

重播缓冲区的存储。

存储必须是 Storage 的实例。

property writer¶

重播缓冲区的写入器。

写入器必须是 Writer 的实例。

RobosetExperienceReplay¶

文档

教程

资源