注意

转到结尾下载完整的示例代码。

TorchRL 简介¶

此演示在 ICML 2022 行业演示日上展示。

它很好地概述了 TorchRL 的功能。如果您有任何疑问或意见，请随时联系 vmoens@fb.com 或提交问题。

TorchRL 是一个用于 PyTorch 的开源强化学习 (RL) 库。

PyTorch 生态系统团队 (Meta) 已决定投资该库，以提供一个领先的平台来在研究环境中开发 RL 解决方案。

它提供 PyTorch 和以 Python 为先的、低级和高级抽象 # 用于 RL，旨在高效、有文档记录并经过适当测试。代码旨在支持 RL 方面的研究。大部分代码都是以高度模块化的方式用 Python 编写的，因此研究人员可以轻松地交换组件、转换它们或编写新的组件，而无需花费太多精力。

此存储库试图与现有的 PyTorch 生态系统库保持一致，因为它具有数据集支柱 (torchrl/envs)、转换、模型、数据实用程序（例如收集器和容器）等。TorchRL 旨在尽可能减少依赖项（Python 标准库、NumPy 和 PyTorch）。常见的环境库（例如 OpenAI Gym）仅是可选的。

内容:

与其他领域不同，RL 更关注算法而不是媒体。因此，很难创建真正独立的组件。

TorchRL 不是什么

算法集合：我们不打算提供 RL 算法的 SOTA 实现，但我们仅提供这些算法作为如何使用该库的示例。
研究框架：TorchRL 中的模块化有两种风格。首先，我们尝试构建可重用的组件，以便它们可以轻松地相互交换。其次，我们尽最大努力使组件能够独立于库的其余部分使用。

TorchRL 只有很少的核心依赖项，主要是 PyTorch 和 NumPy。所有其他依赖项（Gym、Torchvision、WandB/TensorBoard）都是可选的。

数据¶

TensorDict¶

import torch
from tensordict import TensorDict

让我们创建一个 TensorDict。

batch_size = 5
tensordict = TensorDict(
    source={
        "key 1": torch.zeros(batch_size, 3),
        "key 2": torch.zeros(batch_size, 5, 6, dtype=torch.bool),
    },
    batch_size=[batch_size],
)
print(tensordict)

TensorDict(
    fields={
        key 1: Tensor(shape=torch.Size([5, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        key 2: Tensor(shape=torch.Size([5, 5, 6]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([5]),
    device=None,
    is_shared=False)

您可以像查询键一样索引 TensorDict。

print(tensordict[2])
print(tensordict["key 1"] is tensordict.get("key 1"))

TensorDict(
    fields={
        key 1: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        key 2: Tensor(shape=torch.Size([5, 6]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)
True

以下显示了如何堆叠多个 TensorDict。

tensordict1 = TensorDict(
    source={
        "key 1": torch.zeros(batch_size, 1),
        "key 2": torch.zeros(batch_size, 5, 6, dtype=torch.bool),
    },
    batch_size=[batch_size],
)

tensordict2 = TensorDict(
    source={
        "key 1": torch.ones(batch_size, 1),
        "key 2": torch.ones(batch_size, 5, 6, dtype=torch.bool),
    },
    batch_size=[batch_size],
)

tensordict = torch.stack([tensordict1, tensordict2], 0)
tensordict.batch_size, tensordict["key 1"]

(torch.Size([2, 5]), tensor([[[0.],
         [0.],
         [0.],
         [0.],
         [0.]],

        [[1.],
         [1.],
         [1.],
         [1.],
         [1.]]]))

以下是 TensorDict 的一些其他功能。

print(
    "view(-1): ",
    tensordict.view(-1).batch_size,
    tensordict.view(-1).get("key 1").shape,
)

print("to device: ", tensordict.to("cpu"))

# print("pin_memory: ", tensordict.pin_memory())

print("share memory: ", tensordict.share_memory_())

print(
    "permute(1, 0): ",
    tensordict.permute(1, 0).batch_size,
    tensordict.permute(1, 0).get("key 1").shape,
)

print(
    "expand: ",
    tensordict.expand(3, *tensordict.batch_size).batch_size,
    tensordict.expand(3, *tensordict.batch_size).get("key 1").shape,
)

view(-1):  torch.Size([10]) torch.Size([10, 1])
to device:  TensorDict(
    fields={
        key 1: Tensor(shape=torch.Size([2, 5, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        key 2: Tensor(shape=torch.Size([2, 5, 5, 6]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([2, 5]),
    device=cpu,
    is_shared=False)
share memory:  TensorDict(
    fields={
        key 1: Tensor(shape=torch.Size([2, 5, 1]), device=cpu, dtype=torch.float32, is_shared=True),
        key 2: Tensor(shape=torch.Size([2, 5, 5, 6]), device=cpu, dtype=torch.bool, is_shared=True)},
    batch_size=torch.Size([2, 5]),
    device=None,
    is_shared=True)
permute(1, 0):  torch.Size([5, 2]) torch.Size([5, 2, 1])
expand:  torch.Size([3, 2, 5]) torch.Size([3, 2, 5, 1])

您也可以创建嵌套 TensorDict。

tensordict = TensorDict(
    source={
        "key 1": torch.zeros(batch_size, 3),
        "key 2": TensorDict(
            source={"sub-key 1": torch.zeros(batch_size, 2, 1)},
            batch_size=[batch_size, 2],
        ),
    },
    batch_size=[batch_size],
)
tensordict

TensorDict(
    fields={
        key 1: Tensor(shape=torch.Size([5, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        key 2: TensorDict(
            fields={
                sub-key 1: Tensor(shape=torch.Size([5, 2, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([5, 2]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([5]),
    device=None,
    is_shared=False)

回放缓冲区¶

from torchrl.data import PrioritizedReplayBuffer, ReplayBuffer

rb = ReplayBuffer(collate_fn=lambda x: x)
rb.add(1)
rb.sample(1)

[1]

rb.extend([2, 3])
rb.sample(3)

[2, 1, 3]

rb = PrioritizedReplayBuffer(alpha=0.7, beta=1.1, collate_fn=lambda x: x)
rb.add(1)
rb.sample(1)
rb.update_priority(1, 0.5)

以下是使用带有张量字典的回放缓冲区的示例。

collate_fn = torch.stack
rb = ReplayBuffer(collate_fn=collate_fn)
rb.add(TensorDict({"a": torch.randn(3)}, batch_size=[]))
len(rb)

rb.extend(TensorDict({"a": torch.randn(2, 3)}, batch_size=[2]))
print(len(rb))
print(rb.sample(10))
print(rb.sample(2).contiguous())

3
TensorDict(
    fields={
        a: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([10]),
    device=None,
    is_shared=False)
TensorDict(
    fields={
        a: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([2]),
    device=None,
    is_shared=False)

torch.manual_seed(0)
from torchrl.data import TensorDictPrioritizedReplayBuffer

rb = TensorDictPrioritizedReplayBuffer(alpha=0.7, beta=1.1, priority_key="td_error")
rb.extend(TensorDict({"a": torch.randn(2, 3)}, batch_size=[2]))
tensordict_sample = rb.sample(2).contiguous()
tensordict_sample

TensorDict(
    fields={
        _weight: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False),
        a: Tensor(shape=torch.Size([2, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        index: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.int64, is_shared=False)},
    batch_size=torch.Size([2]),
    device=None,
    is_shared=False)

tensordict_sample["index"]

tensor([0, 0])

tensordict_sample["td_error"] = torch.rand(2)
rb.update_tensordict_priority(tensordict_sample)

for i, val in enumerate(rb._sampler._sum_tree):
    print(i, val)
    if i == len(rb):
        break

try:
    import gymnasium as gym
except ModuleNotFoundError:
    import gym

0.28791671991348267
1.0
0.0

环境¶

from torchrl.envs.libs.gym import GymEnv, GymWrapper

gym_env = gym.make("Pendulum-v1")
env = GymWrapper(gym_env)
env = GymEnv("Pendulum-v1")

tensordict = env.reset()
env.rand_step(tensordict)

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False),
        done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([]),
            device=None,
            is_shared=False),
        observation: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)

更改环境配置¶

env = GymEnv("Pendulum-v1", frame_skip=3, from_pixels=True, pixels_only=False)
env.reset()

TensorDict(
    fields={
        done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        observation: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        pixels: Tensor(shape=torch.Size([500, 500, 3]), device=cpu, dtype=torch.uint8, is_shared=False),
        terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)

env.close()
del env

from torchrl.envs import (
    Compose,
    NoopResetEnv,
    ObservationNorm,
    ToTensorImage,
    TransformedEnv,
)

base_env = GymEnv("Pendulum-v1", frame_skip=3, from_pixels=True, pixels_only=False)
env = TransformedEnv(base_env, Compose(NoopResetEnv(3), ToTensorImage()))
env.append_transform(ObservationNorm(in_keys=["pixels"], loc=2, scale=1))

TransformedEnv(
    env=GymEnv(env=Pendulum-v1, batch_size=torch.Size([]), device=None),
    transform=Compose(
            NoopResetEnv(noops=3, random=True),
            ToTensorImage(keys=['pixels']),
            ObservationNorm(loc=2.0000, scale=1.0000, keys=['pixels'])))

转换¶

from torchrl.envs import (
    Compose,
    NoopResetEnv,
    ObservationNorm,
    StepCounter,
    ToTensorImage,
    TransformedEnv,
)

base_env = GymEnv("Pendulum-v1", frame_skip=3, from_pixels=True, pixels_only=False)
env = TransformedEnv(base_env, Compose(NoopResetEnv(3), ToTensorImage()))
env.append_transform(ObservationNorm(in_keys=["pixels"], loc=2, scale=1))

TransformedEnv(
    env=GymEnv(env=Pendulum-v1, batch_size=torch.Size([]), device=None),
    transform=Compose(
            NoopResetEnv(noops=3, random=True),
            ToTensorImage(keys=['pixels']),
            ObservationNorm(loc=2.0000, scale=1.0000, keys=['pixels'])))

env.reset()

TensorDict(
    fields={
        done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        observation: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        pixels: Tensor(shape=torch.Size([3, 500, 500]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)

print("env: ", env)
print("last transform parent: ", env.transform[2].parent)

env:  TransformedEnv(
    env=GymEnv(env=Pendulum-v1, batch_size=torch.Size([]), device=None),
    transform=Compose(
            NoopResetEnv(noops=3, random=True),
            ToTensorImage(keys=['pixels']),
            ObservationNorm(loc=2.0000, scale=1.0000, keys=['pixels'])))
last transform parent:  TransformedEnv(
    env=GymEnv(env=Pendulum-v1, batch_size=torch.Size([]), device=None),
    transform=Compose(
            NoopResetEnv(noops=3, random=True),
            ToTensorImage(keys=['pixels'])))

矢量化环境¶

from torchrl.envs import ParallelEnv

base_env = ParallelEnv(
    4,
    lambda: GymEnv("Pendulum-v1", frame_skip=3, from_pixels=True, pixels_only=False),
    mp_start_method="fork",  # This will break on Windows machines! Remove and decorate with if __name__ == "__main__"
)
env = TransformedEnv(
    base_env, Compose(StepCounter(), ToTensorImage())
)  # applies transforms on batch of envs
env.append_transform(ObservationNorm(in_keys=["pixels"], loc=2, scale=1))
env.reset()

TensorDict(
    fields={
        done: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        observation: Tensor(shape=torch.Size([4, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        pixels: Tensor(shape=torch.Size([4, 3, 500, 500]), device=cpu, dtype=torch.float32, is_shared=False),
        step_count: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.int64, is_shared=False),
        terminated: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([4]),
    device=None,
    is_shared=False)

print(env.action_spec)

env.close()
del env

BoundedTensorSpec(
    shape=torch.Size([4, 1]),
    space=ContinuousBox(
        low=Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.float32, contiguous=True),
        high=Tensor(shape=torch.Size([4, 1]), device=cpu, dtype=torch.float32, contiguous=True)),
    device=cpu,
    dtype=torch.float32,
    domain=continuous)

模块¶

模型¶

MLP 模型示例

from torch import nn

from torchrl.modules import ConvNet, MLP
from torchrl.modules.models.utils import SquashDims

net = MLP(num_cells=[32, 64], out_features=4, activation_class=nn.ELU)
print(net)
print(net(torch.randn(10, 3)).shape)

MLP(
  (0): LazyLinear(in_features=0, out_features=32, bias=True)
  (1): ELU(alpha=1.0)
  (2): Linear(in_features=32, out_features=64, bias=True)
  (3): ELU(alpha=1.0)
  (4): Linear(in_features=64, out_features=4, bias=True)
)
torch.Size([10, 4])

CNN 模型示例

cnn = ConvNet(
    num_cells=[32, 64],
    kernel_sizes=[8, 4],
    strides=[2, 1],
    aggregator_class=SquashDims,
)
print(cnn)
print(cnn(torch.randn(10, 3, 32, 32)).shape)  # last tensor is squashed

ConvNet(
  (0): LazyConv2d(0, 32, kernel_size=(8, 8), stride=(2, 2))
  (1): ELU(alpha=1.0)
  (2): Conv2d(32, 64, kernel_size=(4, 4), stride=(1, 1))
  (3): ELU(alpha=1.0)
  (4): SquashDims()
)
torch.Size([10, 6400])

TensorDictModules¶

from tensordict.nn import TensorDictModule

tensordict = TensorDict({"key 1": torch.randn(10, 3)}, batch_size=[10])
module = nn.Linear(3, 4)
td_module = TensorDictModule(module, in_keys=["key 1"], out_keys=["key 2"])
td_module(tensordict)
print(tensordict)

TensorDict(
    fields={
        key 1: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        key 2: Tensor(shape=torch.Size([10, 4]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([10]),
    device=None,
    is_shared=False)

模块序列¶

from tensordict.nn import TensorDictSequential

backbone_module = nn.Linear(5, 3)
backbone = TensorDictModule(
    backbone_module, in_keys=["observation"], out_keys=["hidden"]
)
actor_module = nn.Linear(3, 4)
actor = TensorDictModule(actor_module, in_keys=["hidden"], out_keys=["action"])
value_module = MLP(out_features=1, num_cells=[4, 5])
value = TensorDictModule(value_module, in_keys=["hidden", "action"], out_keys=["value"])

sequence = TensorDictSequential(backbone, actor, value)
print(sequence)

TensorDictSequential(
    module=ModuleList(
      (0): TensorDictModule(
          module=Linear(in_features=5, out_features=3, bias=True),
          device=cpu,
          in_keys=['observation'],
          out_keys=['hidden'])
      (1): TensorDictModule(
          module=Linear(in_features=3, out_features=4, bias=True),
          device=cpu,
          in_keys=['hidden'],
          out_keys=['action'])
      (2): TensorDictModule(
          module=MLP(
            (0): LazyLinear(in_features=0, out_features=4, bias=True)
            (1): Tanh()
            (2): Linear(in_features=4, out_features=5, bias=True)
            (3): Tanh()
            (4): Linear(in_features=5, out_features=1, bias=True)
          ),
          device=cpu,
          in_keys=['hidden', 'action'],
          out_keys=['value'])
    ),
    device=cpu,
    in_keys=['observation'],
    out_keys=['hidden', 'action', 'value'])

print(sequence.in_keys, sequence.out_keys)

['observation'] ['hidden', 'action', 'value']

tensordict = TensorDict(
    {"observation": torch.randn(3, 5)},
    [3],
)
backbone(tensordict)
actor(tensordict)
value(tensordict)

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        hidden: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        observation: Tensor(shape=torch.Size([3, 5]), device=cpu, dtype=torch.float32, is_shared=False),
        value: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

tensordict = TensorDict(
    {"observation": torch.randn(3, 5)},
    [3],
)
sequence(tensordict)
print(tensordict)

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        hidden: Tensor(shape=torch.Size([3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        observation: Tensor(shape=torch.Size([3, 5]), device=cpu, dtype=torch.float32, is_shared=False),
        value: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

函数式编程（集成/元 RL）¶

from tensordict import TensorDict

params = TensorDict.from_module(sequence)
print("extracted params", params)

extracted params TensorDict(
    fields={
        module: TensorDict(
            fields={
                0: TensorDict(
                    fields={
                        module: TensorDict(
                            fields={
                                bias: Parameter(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
                                weight: Parameter(shape=torch.Size([3, 5]), device=cpu, dtype=torch.float32, is_shared=False)},
                            batch_size=torch.Size([]),
                            device=None,
                            is_shared=False)},
                    batch_size=torch.Size([]),
                    device=None,
                    is_shared=False),
                1: TensorDict(
                    fields={
                        module: TensorDict(
                            fields={
                                bias: Parameter(shape=torch.Size([4]), device=cpu, dtype=torch.float32, is_shared=False),
                                weight: Parameter(shape=torch.Size([4, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
                            batch_size=torch.Size([]),
                            device=None,
                            is_shared=False)},
                    batch_size=torch.Size([]),
                    device=None,
                    is_shared=False),
                2: TensorDict(
                    fields={
                        module: TensorDict(
                            fields={
                                0: TensorDict(
                                    fields={
                                        bias: Parameter(shape=torch.Size([4]), device=cpu, dtype=torch.float32, is_shared=False),
                                        weight: Parameter(shape=torch.Size([4, 7]), device=cpu, dtype=torch.float32, is_shared=False)},
                                    batch_size=torch.Size([]),
                                    device=None,
                                    is_shared=False),
                                2: TensorDict(
                                    fields={
                                        bias: Parameter(shape=torch.Size([5]), device=cpu, dtype=torch.float32, is_shared=False),
                                        weight: Parameter(shape=torch.Size([5, 4]), device=cpu, dtype=torch.float32, is_shared=False)},
                                    batch_size=torch.Size([]),
                                    device=None,
                                    is_shared=False),
                                4: TensorDict(
                                    fields={
                                        bias: Parameter(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False),
                                        weight: Parameter(shape=torch.Size([1, 5]), device=cpu, dtype=torch.float32, is_shared=False)},
                                    batch_size=torch.Size([]),
                                    device=None,
                                    is_shared=False)},
                            batch_size=torch.Size([]),
                            device=None,
                            is_shared=False)},
                    batch_size=torch.Size([]),
                    device=None,
                    is_shared=False)},
            batch_size=torch.Size([]),
            device=None,
            is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)

使用张量字典进行函数调用

with params.to_module(sequence):
    sequence(tensordict)

使用矢量化映射进行模型集成

from torch import vmap

params_expand = params.expand(4)

def exec_sequence(params, data):
    with params.to_module(sequence):
        return sequence(data)

tensordict_exp = vmap(exec_sequence, (0, None))(params_expand, tensordict)
print(tensordict_exp)

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([4, 3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        hidden: Tensor(shape=torch.Size([4, 3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        observation: Tensor(shape=torch.Size([4, 3, 5]), device=cpu, dtype=torch.float32, is_shared=False),
        value: Tensor(shape=torch.Size([4, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([4, 3]),
    device=None,
    is_shared=False)

专门类¶

torch.manual_seed(0)
from torchrl.data import BoundedTensorSpec
from torchrl.modules import SafeModule

spec = BoundedTensorSpec(-torch.ones(3), torch.ones(3))
base_module = nn.Linear(5, 3)
module = SafeModule(
    module=base_module, spec=spec, in_keys=["obs"], out_keys=["action"], safe=True
)
tensordict = TensorDict({"obs": torch.randn(5)}, batch_size=[])
module(tensordict)["action"]

tensor([-0.0137,  0.1524, -0.0641], grad_fn=<ViewBackward0>)

tensordict = TensorDict({"obs": torch.randn(5) * 100}, batch_size=[])
module(tensordict)["action"]  # safe=True projects the result within the set

tensor([-1.,  1., -1.], grad_fn=<AsStridedBackward0>)

from torchrl.modules import Actor

base_module = nn.Linear(5, 3)
actor = Actor(base_module, in_keys=["obs"])
tensordict = TensorDict({"obs": torch.randn(5)}, batch_size=[])
actor(tensordict)  # action is the default value

from tensordict.nn import (
    ProbabilisticTensorDictModule,
    ProbabilisticTensorDictSequential,
)

# Probabilistic modules
from torchrl.modules import NormalParamExtractor, TanhNormal

td = TensorDict({"input": torch.randn(3, 5)}, [3])
net = nn.Sequential(
    nn.Linear(5, 4), NormalParamExtractor()
)  # splits the output in loc and scale
module = TensorDictModule(net, in_keys=["input"], out_keys=["loc", "scale"])
td_module = ProbabilisticTensorDictSequential(
    module,
    ProbabilisticTensorDictModule(
        in_keys=["loc", "scale"],
        out_keys=["action"],
        distribution_class=TanhNormal,
        return_log_prob=False,
    ),
)
td_module(td)
print(td)

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 2]), device=cpu, dtype=torch.float32, is_shared=False),
        input: Tensor(shape=torch.Size([3, 5]), device=cpu, dtype=torch.float32, is_shared=False),
        loc: Tensor(shape=torch.Size([3, 2]), device=cpu, dtype=torch.float32, is_shared=False),
        scale: Tensor(shape=torch.Size([3, 2]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

# returning the log-probability
td = TensorDict({"input": torch.randn(3, 5)}, [3])
td_module = ProbabilisticTensorDictSequential(
    module,
    ProbabilisticTensorDictModule(
        in_keys=["loc", "scale"],
        out_keys=["action"],
        distribution_class=TanhNormal,
        return_log_prob=True,
    ),
)
td_module(td)
print(td)

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 2]), device=cpu, dtype=torch.float32, is_shared=False),
        input: Tensor(shape=torch.Size([3, 5]), device=cpu, dtype=torch.float32, is_shared=False),
        loc: Tensor(shape=torch.Size([3, 2]), device=cpu, dtype=torch.float32, is_shared=False),
        sample_log_prob: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        scale: Tensor(shape=torch.Size([3, 2]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

# Sampling vs mode / mean
from torchrl.envs.utils import ExplorationType, set_exploration_type

td = TensorDict({"input": torch.randn(3, 5)}, [3])

torch.manual_seed(0)
with set_exploration_type(ExplorationType.RANDOM):
    td_module(td)
    print("random:", td["action"])

with set_exploration_type(ExplorationType.MODE):
    td_module(td)
    print("mode:", td["action"])

with set_exploration_type(ExplorationType.MODE):
    td_module(td)
    print("mean:", td["action"])

random: tensor([[ 0.8728, -0.1334],
        [-0.9833,  0.3494],
        [-0.6887, -0.6402]], grad_fn=<_SafeTanhBackward>)
mode: tensor([[-0.1132,  0.1762],
        [-0.3430, -0.2668],
        [ 0.2918,  0.6239]], grad_fn=<_SafeTanhBackward>)
mean: tensor([[-0.1132,  0.1762],
        [-0.3430, -0.2668],
        [ 0.2918,  0.6239]], grad_fn=<_SafeTanhBackward>)

使用环境和模块¶

from torchrl.envs.utils import step_mdp

env = GymEnv("Pendulum-v1")

action_spec = env.action_spec
actor_module = nn.Linear(3, 1)
actor = SafeModule(
    actor_module, spec=action_spec, in_keys=["observation"], out_keys=["action"]
)

torch.manual_seed(0)
env.set_seed(0)

max_steps = 100
tensordict = env.reset()
tensordicts = TensorDict({}, [max_steps])
for i in range(max_steps):
    actor(tensordict)
    tensordicts[i] = env.step(tensordict)
    if tensordict["done"].any():
        break
    tensordict = step_mdp(tensordict)  # roughly equivalent to obs = next_obs

tensordicts_prealloc = tensordicts.clone()
print("total steps:", i)
print(tensordicts)

total steps: 99
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        done: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([100, 3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([100]),
            device=None,
            is_shared=False),
        observation: Tensor(shape=torch.Size([100, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([100]),
    device=None,
    is_shared=False)

# equivalent
torch.manual_seed(0)
env.set_seed(0)

max_steps = 100
tensordict = env.reset()
tensordicts = []
for _ in range(max_steps):
    actor(tensordict)
    tensordicts.append(env.step(tensordict))
    if tensordict["done"].any():
        break
    tensordict = step_mdp(tensordict)  # roughly equivalent to obs = next_obs
tensordicts_stack = torch.stack(tensordicts, 0)
print("total steps:", i)
print(tensordicts_stack)

total steps: 99
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        done: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([100, 3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([100]),
            device=None,
            is_shared=False),
        observation: Tensor(shape=torch.Size([100, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([100]),
    device=None,
    is_shared=False)

(tensordicts_stack == tensordicts_prealloc).all()

True

torch.manual_seed(0)
env.set_seed(0)
tensordict_rollout = env.rollout(policy=actor, max_steps=max_steps)
tensordict_rollout


(tensordict_rollout == tensordicts_prealloc).all()

from tensordict.nn import TensorDictModule

收集器¶

from torchrl.collectors import MultiaSyncDataCollector, MultiSyncDataCollector

from torchrl.envs import EnvCreator, SerialEnv
from torchrl.envs.libs.gym import GymEnv

EnvCreator 确保我们可以从一个进程发送 lambda 函数到另一个进程。出于简单起见，我们使用 SerialEnv，但对于较大的作业，ParallelEnv 更适合。

parallel_env = SerialEnv(
    3,
    EnvCreator(lambda: GymEnv("Pendulum-v1")),
)
create_env_fn = [parallel_env, parallel_env]

actor_module = nn.Linear(3, 1)
actor = TensorDictModule(actor_module, in_keys=["observation"], out_keys=["action"])

同步数据收集器

devices = ["cpu", "cpu"]

collector = MultiSyncDataCollector(
    create_env_fn=create_env_fn,  # either a list of functions or a ParallelEnv
    policy=actor,
    total_frames=240,
    max_frames_per_traj=-1,  # envs are terminating, we don't need to stop them early
    frames_per_batch=60,  # we want 60 frames at a time (we have 3 envs per sub-collector)
    device=devices,
)

for i, d in enumerate(collector):
    if i == 0:
        print(d)  # trajectories are split automatically in [6 workers x 10 steps]
    collector.update_policy_weights_()  # make sure that our policies have the latest weights if working on multiple devices
print(i)
collector.shutdown()
del collector

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([2, 3, 10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        collector: TensorDict(
            fields={
                traj_ids: Tensor(shape=torch.Size([2, 3, 10]), device=cpu, dtype=torch.int64, is_shared=False)},
            batch_size=torch.Size([2, 3, 10]),
            device=cpu,
            is_shared=False),
        done: Tensor(shape=torch.Size([2, 3, 10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([2, 3, 10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([2, 3, 10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([2, 3, 10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([2, 3, 10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([2, 3, 10, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([2, 3, 10]),
            device=cpu,
            is_shared=False),
        observation: Tensor(shape=torch.Size([2, 3, 10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([2, 3, 10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([2, 3, 10, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([2, 3, 10]),
    device=cpu,
    is_shared=False)
3

# async data collector: keeps working while you update your model
collector = MultiaSyncDataCollector(
    create_env_fn=create_env_fn,  # either a list of functions or a ParallelEnv
    policy=actor,
    total_frames=240,
    max_frames_per_traj=-1,  # envs are terminating, we don't need to stop them early
    frames_per_batch=60,  # we want 60 frames at a time (we have 3 envs per sub-collector)
    device=devices,
)

for i, d in enumerate(collector):
    if i == 0:
        print(d)  # trajectories are split automatically in [6 workers x 10 steps]
    collector.update_policy_weights_()  # make sure that our policies have the latest weights if working on multiple devices
print(i)
collector.shutdown()
del collector
del create_env_fn
del parallel_env

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        collector: TensorDict(
            fields={
                traj_ids: Tensor(shape=torch.Size([3, 20]), device=cpu, dtype=torch.int64, is_shared=False)},
            batch_size=torch.Size([3, 20]),
            device=cpu,
            is_shared=False),
        done: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([3, 20, 3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([3, 20]),
            device=cpu,
            is_shared=False),
        observation: Tensor(shape=torch.Size([3, 20, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([3, 20, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([3, 20]),
    device=cpu,
    is_shared=False)
3

目标¶

# TorchRL delivers meta-RL compatible loss functions
# Disclaimer: This APi may change in the future
from torchrl.objectives import DDPGLoss

actor_module = nn.Linear(3, 1)
actor = TensorDictModule(actor_module, in_keys=["observation"], out_keys=["action"])


class ConcatModule(nn.Linear):
    def forward(self, obs, action):
        return super().forward(torch.cat([obs, action], -1))


value_module = ConcatModule(4, 1)
value = TensorDictModule(
    value_module, in_keys=["observation", "action"], out_keys=["state_action_value"]
)

loss_fn = DDPGLoss(actor, value)
loss_fn.make_value_estimator(loss_fn.default_value_estimator, gamma=0.99)

tensordict = TensorDict(
    {
        "observation": torch.randn(10, 3),
        "next": {
            "observation": torch.randn(10, 3),
            "reward": torch.randn(10, 1),
            "done": torch.zeros(10, 1, dtype=torch.bool),
        },
        "action": torch.randn(10, 1),
    },
    batch_size=[10],
    device="cpu",
)
loss_td = loss_fn(tensordict)

print(loss_td)

TensorDict(
    fields={
        loss_actor: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        loss_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        pred_value: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
        pred_value_max: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        target_value: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
        target_value_max: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        td_error: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)

print(tensordict)

TensorDict(
    fields={
        action: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                observation: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
            batch_size=torch.Size([10]),
            device=cpu,
            is_shared=False),
        observation: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
        td_error: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([10]),
    device=cpu,
    is_shared=False)

库的状态¶

TorchRL 目前处于 alpha 版本：可能存在错误，并且不保证不会出现重大更改。我们应该能够在今年年底之前发布 beta 版本。我们实现这一目标的路线图包括

分布式解决方案
离线 RL
对元 RL 的更大支持
多任务和分层 RL

贡献¶

我们正在积极寻找贡献者和早期用户。如果您正在从事 RL 工作（或只是好奇），请尝试一下！给我们反馈：TorchRL 的成功取决于它对研究人员需求的满足程度。为此，我们需要他们的意见！由于该库尚处于起步阶段，因此这是一个塑造您想要的方式的好时机！

安装库¶

该库位于 PyPI 上：pip install torchrl

脚本的总运行时间：（3 分 43.926 秒）

估计内存使用量：324 MB

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