MinariExperienceReplay¶

class torchrl.data.datasets.MinariExperienceReplay(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)[源代码]¶

Minari 体验回放数据集。

在 Minari 网站上了解更多信息： https://minari.farama.org/

数据格式遵循 TED 约定。

参数:

dataset_id (str) – 要下载的数据集。必须是 MinariExperienceReplay.available_datasets 中的一个。
batch_size (int) – 采样时使用的批处理大小。如果需要，可以通过 data.sample(batch_size) 覆盖。

关键字参数:

root (Path 或 str, 可选) – Minari 数据集根目录。实际的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/atari。minari`。
download (bool 或 str, 可选) – 如果找不到数据集，是否应下载。默认为 True。download 也可以设置为 "force"，在这种情况下，将覆盖下载的数据。
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) – 将样本列表合并以形成 Tensor(s)/输出的 mini-batch。在从 map 风格的数据集进行批处理加载时使用。
pin_memory (bool) – 是否应对 rb 样本调用 pin_memory()。
prefetch (int, 可选) – 使用多线程预取的下一个批次数。
transform (Transform, 可选) – 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分割并填充以具有匹配的形状。要分割轨迹，将使用 "done" 信号，该信号通过 done = truncated | terminated 恢复。换句话说，假定任何 truncated 或 terminated 信号都等同于轨迹的结束。默认为 False。

变量:

available_datasets – 可接受下载条目的列表。

注意

文本数据目前已从包装的数据集中丢弃，因为没有 PyTorch 原生方法来表示文本数据。如果需要此功能，请在 TorchRL 的 GitHub 存储库上发帖。

示例

>>> from torchrl.data.datasets.minari_data import MinariExperienceReplay
>>> data = MinariExperienceReplay("door-human-v1", batch_size=32, download="force")
>>> for sample in data:
...     torchrl_logger.info(sample)
...     break
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([32, 28]), device=cpu, dtype=torch.float32, is_shared=False),
        index: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.int64, is_shared=False),
        info: TensorDict(
            fields={
                success: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([32]),
            device=cpu,
            is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                info: TensorDict(
                    fields={
                        success: Tensor(shape=torch.Size([32]), device=cpu, dtype=torch.bool, is_shared=False)},
                    batch_size=torch.Size([32]),
                    device=cpu,
                    is_shared=False),
                observation: Tensor(shape=torch.Size([32, 39]), device=cpu, dtype=torch.float64, is_shared=False),
                reward: Tensor(shape=torch.Size([32, 1]), device=cpu, dtype=torch.float64, is_shared=False),
                state: TensorDict(
                    fields={
                        door_body_pos: Tensor(shape=torch.Size([32, 3]), device=cpu, dtype=torch.float64, is_shared=False),
                        qpos: Tensor(shape=torch.Size([32, 30]), device=cpu, dtype=torch.float64, is_shared=False),
                        qvel: Tensor(shape=torch.Size([32, 30]), device=cpu, dtype=torch.float64, is_shared=False)},
                    batch_size=torch.Size([32]),
                    device=cpu,
                    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, 39]), device=cpu, dtype=torch.float64, is_shared=False),
        state: TensorDict(
            fields={
                door_body_pos: Tensor(shape=torch.Size([32, 3]), device=cpu, dtype=torch.float64, is_shared=False),
                qpos: Tensor(shape=torch.Size([32, 30]), device=cpu, dtype=torch.float64, is_shared=False),
                qvel: Tensor(shape=torch.Size([32, 30]), device=cpu, dtype=torch.float64, is_shared=False)},
            batch_size=torch.Size([32]),
            device=cpu,
            is_shared=False)},
    batch_size=torch.Size([32]),
    device=cpu,
    is_shared=False)

add(data: TensorDictBase) → int¶

将单个元素添加到重放缓冲区。

参数:: data (Any) – 要添加到重放缓冲区的数据
返回:: 数据在重放缓冲区中的索引。

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

将变换附加到末尾。

调用 sample 时按顺序应用变换。

参数:: transform (Transform) – 要附加的变换
关键字参数:: invert (bool, 可选) – 如果为 True，则在写入时调用转换的 forward 方法，在读取时调用 inverse 方法。默认为 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()

classmethod as_remote(remote_config=None)¶

创建一个远程 ray 类的实例。

参数:

cls (Python Class) – 要远程实例化的类。
remote_config (dict) – 为该类保留的 CPU 核心数量。默认为 torchrl.collectors.distributed.ray.DEFAULT_REMOTE_CLASS_CONFIG。

返回:

一个创建 ray 远程类实例的函数。

property batch_size¶

重放缓冲区的批次大小。

批处理大小可以通过在 sample() 方法中设置 batch_size 参数来覆盖。

它定义了 sample() 返回的样本数量，以及 ReplayBuffer 迭代器生成的样本数量。

property data_path: Path¶: 数据集路径，包括分割。

property data_path_root: Path¶: 数据集根目录路径。

delete()¶: 从磁盘删除数据集存储。

dump(*args, **kwargs)¶: 别名 dumps()。

dumps(path)¶

将重放缓冲区保存到指定路径的磁盘上。

参数:: path (Path 或 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(empty_write_count: bool = True)¶

清空重放缓冲区并将游标重置为 0。

参数:: empty_write_count (bool, optional) – 是否清空 write_count 属性。默认为 True。

extend(tensordicts: TensorDictBase, *, update_priority: bool | None = None) → torch.Tensor¶

使用数据批次扩展重放缓冲区。

参数:: tensordicts (TensorDictBase) – 用于扩展重放缓冲区的数据。
关键字参数:: update_priority (bool, optional) – 是否更新数据的优先级。默认为 True。
返回:: 已添加到重放缓冲区的数据的索引。

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

插入变换。

调用 sample 时按顺序执行变换。

参数:

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

关键字参数:

invert (bool, 可选) – 如果为 True，则在写入时调用转换的 forward 方法，在读取时调用 inverse 方法。默认为 False。

load(*args, **kwargs)¶: 别名 loads()。

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 或 str) – 重放缓冲区保存的路径。

有关更多信息，请参见 dumps()。

next()¶

返回重放缓冲区的下一个项。

此方法用于在 __iter__ 不可用的情况下迭代重放缓冲区，例如 RayReplayBuffer。

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一个包含格式化数据的新存储。

数据转换必须是单位化的（作用于数据集的单个样本）。

Args 和 Keyword Args 会转发给 map()。

数据集随后可以使用 delete() 删除。

关键字参数:

dest (path 或 等价物) – 新数据集位置的路径。
num_frames (int, 可选) – 如果提供，则仅转换前 num_frames 帧。这对于调试转换很有用。

返回：一个将在 ReplayBuffer 实例中使用的新的存储。

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

钩子目前不会在保存重放缓冲区时序列化：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

钩子目前不会在保存重放缓冲区时序列化：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 = None) → TensorDictBase¶

从重放缓冲区中采样数据批次。

使用 Sampler 采样索引，并从 Storage 中检索它们。

参数:

batch_size (int, optional) – 要收集的数据的大小。如果未提供，此方法将采样由采样器指示的批次大小。
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:

一个包含在重放缓冲区中选择的数据批次的 tensordict。如果 return_info 标志设置为 True，则包含此 tensordict 和信息的元组。

property sampler¶

重放缓冲区的采样器。

采样器必须是 Sampler 的实例。

save(*args, **kwargs)¶: 别名 dumps()。

set_sampler(sampler: Sampler)¶: 在重放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)¶

在重放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 (callable, optional) – 如果提供，collate_fn 将设置为此值。否则，它将被重置为默认值。

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

property storage¶

重放缓冲区的存储。

存储器必须是 Storage 的实例。

property write_count¶: 通过 add 和 extend 写入缓冲区的总项数。

property writer¶

重放缓冲区的写入器。

写入器必须是 Writer 的实例。

MinariExperienceReplay¶

文档

教程

资源