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Pendulum：使用 TorchRL 编写您的环境和变换¶
创建环境（模拟器或物理控制系统的接口）是强化学习和控制工程不可或缺的一部分。
TorchRL 提供了一组工具，可以在多种情况下执行此操作。本教程演示了如何使用 PyTorch 和 TorchRL 从头开始编写一个钟摆模拟器。它自由地借鉴了 OpenAI-Gym/Farama-Gymnasium 控制库 中的 Pendulum-v1 实现。
简单钟摆¶
主要学习内容
如何在 TorchRL 中设计环境： - 编写规范（输入、观察和奖励）； - 实现行为：播种、重置和步进。
转换您的环境输入和输出，并编写您自己的变换；
如何使用
TensorDict通过codebase携带任意数据结构。在此过程中，我们将接触到 TorchRL 的三个关键组件
为了让您了解使用 TorchRL 环境可以实现什么，我们将设计一个无状态环境。有状态环境跟踪遇到的最新物理状态，并依靠它来模拟状态到状态的转换，而无状态环境则期望在每个步骤中向其提供当前状态，以及采取的动作。TorchRL 支持这两种类型的环境，但无状态环境更通用，因此涵盖了 TorchRL 环境 API 的更广泛功能。
建模无状态环境使用户可以完全控制模拟器的输入和输出：可以随时重置实验或从外部主动修改动态。但是，它假设我们对任务有一定的控制权，但情况可能并非总是如此：解决我们无法控制当前状态的问题更具挑战性，但具有更广泛的应用。
无状态环境的另一个优点是它们可以实现批量执行转换模拟。如果后端和实现允许，则可以在标量、向量或张量上无缝执行代数运算。本教程给出了此类示例。
本教程的结构如下
我们将首先熟悉环境属性：其形状 (
batch_size)、其方法（主要是step()、reset()和set_seed()）以及最终的规范。在编写完模拟器代码后，我们将演示如何在训练期间使用变换来使用它。
我们将探索 TorchRL API 带来的新途径，包括：转换输入的可能性、模拟的向量化执行以及通过模拟图进行反向传播的可能性。
最后，我们将训练一个简单的策略来解决我们实现的系统。
此环境的内置版本可以在类中找到：~torchrl.envs.PendulumEnv。
from collections import defaultdict
from typing import Optional
import numpy as np
import torch
import tqdm
from tensordict import TensorDict, TensorDictBase
from tensordict.nn import TensorDictModule
from torch import nn
from torchrl.data import Bounded, Composite, Unbounded
from torchrl.envs import (
CatTensors,
EnvBase,
Transform,
TransformedEnv,
UnsqueezeTransform,
)
from torchrl.envs.transforms.transforms import _apply_to_composite
from torchrl.envs.utils import check_env_specs, step_mdp
DEFAULT_X = np.pi
DEFAULT_Y = 1.0
设计新的环境类时，您必须注意四件事
EnvBase._reset()，它为模拟器在（可能随机的）初始状态下的重置进行编码；EnvBase._step()，它为状态转换动态进行编码；EnvBase._set_seed()，它实现播种机制；环境规范。
让我们首先描述手头的问题：我们想要建模一个简单的钟摆，我们可以控制施加在其固定点上的扭矩。我们的目标是将钟摆置于向上位置（按照惯例，角位置为 0），并使其在该位置保持静止。为了设计我们的动态系统，我们需要定义两个方程：遵循动作（施加的扭矩）的运动方程和构成我们目标函数的奖励方程。
对于运动方程，我们将按照以下公式更新角速度
\[\dot{\theta}_{t+1} = \dot{\theta}_t + (3 * g / (2 * L) * \sin(\theta_t) + 3 / (m * L^2) * u) * dt\]
其中 \(\dot{\theta}\) 是角速度（单位：弧度/秒），\(g\) 是重力，\(L\) 是钟摆长度，\(m\) 是其质量，\(\theta\) 是其角位置，\(u\) 是扭矩。然后根据以下公式更新角位置
\[\theta_{t+1} = \theta_{t} + \dot{\theta}_{t+1} dt\]
我们将奖励定义为
\[r = -(\theta^2 + 0.1 * \dot{\theta}^2 + 0.001 * u^2)\]
当角度接近 0（钟摆处于向上位置）、角速度接近 0（无运动）且扭矩也为 0 时，奖励将最大化。
编写动作效果代码：_step()¶
step 方法是首先要考虑的事情，因为它将编码我们感兴趣的模拟。在 TorchRL 中，EnvBase 类有一个 EnvBase.step() 方法，该方法接收一个 tensordict.TensorDict 实例，其中包含一个 "action" 条目，指示要采取的操作。
为了方便从该 tensordict 中读取和写入，并确保键与库的预期一致，模拟部分已委托给私有抽象方法 _step()，该方法从 tensordict 读取输入数据，并写入一个包含输出数据的新 tensordict。
_step() 方法应执行以下操作
读取输入键（例如
"action"）并基于这些键执行模拟；检索观察、完成状态和奖励；
将一组观察值以及奖励和完成状态写入新
TensorDict中的相应条目。
接下来，step() 方法会将 step() 的输出合并到输入 tensordict 中，以强制执行输入/输出一致性。
通常，对于有状态环境，它看起来像这样
>>> policy(env.reset())
>>> print(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),
observation: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=cpu,
is_shared=False)
>>> env.step(tensordict)
>>> print(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([]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=cpu,
is_shared=False),
observation: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=cpu,
is_shared=False)
请注意，根 tensordict 没有更改，唯一的修改是出现了一个新的 "next" 条目，其中包含新信息。
在 Pendulum 示例中，我们的 _step() 方法将从输入 tensordict 中读取相关条目，并计算在应用由 "action" 键编码的力后钟摆的位置和速度。我们将钟摆的新角位置 "new_th" 计算为先前位置 "th" 加上新速度 "new_thdot" 在时间间隔 dt 内的结果。
由于我们的目标是将钟摆向上转动并使其在该位置保持静止，因此对于接近目标的位置和低速，我们的 cost（负奖励）函数较低。实际上，我们希望阻止远离“向上”的位置和/或远离 0 的速度。
在我们的示例中，EnvBase._step() 被编码为静态方法，因为我们的环境是无状态的。在有状态设置中，需要 self 参数，因为需要从环境中读取状态。
def _step(tensordict):
th, thdot = tensordict["th"], tensordict["thdot"] # th := theta
g_force = tensordict["params", "g"]
mass = tensordict["params", "m"]
length = tensordict["params", "l"]
dt = tensordict["params", "dt"]
u = tensordict["action"].squeeze(-1)
u = u.clamp(-tensordict["params", "max_torque"], tensordict["params", "max_torque"])
costs = angle_normalize(th) ** 2 + 0.1 * thdot**2 + 0.001 * (u**2)
new_thdot = (
thdot
+ (3 * g_force / (2 * length) * th.sin() + 3.0 / (mass * length**2) * u) * dt
)
new_thdot = new_thdot.clamp(
-tensordict["params", "max_speed"], tensordict["params", "max_speed"]
)
new_th = th + new_thdot * dt
reward = -costs.view(*tensordict.shape, 1)
done = torch.zeros_like(reward, dtype=torch.bool)
out = TensorDict(
{
"th": new_th,
"thdot": new_thdot,
"params": tensordict["params"],
"reward": reward,
"done": done,
},
tensordict.shape,
)
return out
def angle_normalize(x):
return ((x + torch.pi) % (2 * torch.pi)) - torch.pi
重置模拟器：_reset()¶
我们需要关注的第二个方法是 _reset() 方法。与 _step() 类似，它应该在它输出的 tensordict 中写入观察条目，并可能写入完成状态（如果省略完成状态，则父方法 reset() 会将其填充为 False）。在某些情况下，_reset 方法需要接收来自调用它的函数的命令（例如，在多智能体设置中，我们可能想要指示哪些智能体需要重置）。这就是为什么 _reset() 方法也期望 tensordict 作为输入，尽管它可能完全为空或 None。
父 EnvBase.reset() 执行一些简单的检查，例如 EnvBase.step() 所做的检查，例如确保在输出 tensordict 中返回 "done" 状态，并且形状与规范的预期相匹配。
对于我们来说，唯一重要的是考虑 EnvBase._reset() 是否包含所有预期的观察结果。再一次，由于我们正在使用无状态环境，因此我们在名为 "params" 的嵌套 tensordict 中传递钟摆的配置。
在此示例中，我们不传递完成状态，因为对于 _reset() 来说这不是强制性的，并且我们的环境是非终止的，因此我们始终期望它是 False。
def _reset(self, tensordict):
if tensordict is None or tensordict.is_empty():
# if no ``tensordict`` is passed, we generate a single set of hyperparameters
# Otherwise, we assume that the input ``tensordict`` contains all the relevant
# parameters to get started.
tensordict = self.gen_params(batch_size=self.batch_size)
high_th = torch.tensor(DEFAULT_X, device=self.device)
high_thdot = torch.tensor(DEFAULT_Y, device=self.device)
low_th = -high_th
low_thdot = -high_thdot
# for non batch-locked environments, the input ``tensordict`` shape dictates the number
# of simulators run simultaneously. In other contexts, the initial
# random state's shape will depend upon the environment batch-size instead.
th = (
torch.rand(tensordict.shape, generator=self.rng, device=self.device)
* (high_th - low_th)
+ low_th
)
thdot = (
torch.rand(tensordict.shape, generator=self.rng, device=self.device)
* (high_thdot - low_thdot)
+ low_thdot
)
out = TensorDict(
{
"th": th,
"thdot": thdot,
"params": tensordict["params"],
},
batch_size=tensordict.shape,
)
return out
环境元数据：env.*_spec¶
规范定义了环境的输入和输出域。重要的是，规范要准确定义运行时将接收的张量，因为它们通常用于在多处理和分布式设置中携带有关环境的信息。它们还可以用于实例化延迟定义的神经网络和测试脚本，而无需实际查询环境（例如，对于真实世界的物理系统，这可能会很昂贵）。
在我们的环境中，我们必须编写四个规范
EnvBase.observation_spec：这将是一个CompositeSpec实例，其中每个键都是一个观察结果（CompositeSpec可以看作是规范的字典）。EnvBase.action_spec：它可以是任何类型的规范，但它必须与输入tensordict中的"action"条目相对应；EnvBase.reward_spec：提供有关奖励空间的信息；EnvBase.done_spec：提供有关完成标志空间的信息。
TorchRL 规范组织在两个通用容器中：input_spec，其中包含 step 函数读取的信息的规范（在包含动作的 action_spec 和包含所有其余内容的 state_spec 之间划分），以及 output_spec，它编码 step 输出的规范（observation_spec、reward_spec 和 done_spec）。一般来说，您不应直接与 output_spec 和 input_spec 交互，而只能与其内容交互：observation_spec、reward_spec、done_spec、action_spec 和 state_spec。原因是规范在 output_spec 和 input_spec 内以非平凡的方式组织，并且不应直接修改这两者中的任何一个。
换句话说，observation_spec 和相关属性是输出和输入规范容器内容的便捷快捷方式。
TorchRL 提供了多个 TensorSpec 子类 来编码环境的输入和输出特征。
规范形状¶
环境规范的前导维度必须与环境批量大小匹配。这样做是为了强制环境的每个组件（包括其变换）都准确表示预期的输入和输出形状。这应该在有状态设置中准确编码。
对于非批量锁定环境，例如我们示例中的环境（见下文），这无关紧要，因为环境批量大小很可能为空。
def _make_spec(self, td_params):
# Under the hood, this will populate self.output_spec["observation"]
self.observation_spec = Composite(
th=Bounded(
low=-torch.pi,
high=torch.pi,
shape=(),
dtype=torch.float32,
),
thdot=Bounded(
low=-td_params["params", "max_speed"],
high=td_params["params", "max_speed"],
shape=(),
dtype=torch.float32,
),
# we need to add the ``params`` to the observation specs, as we want
# to pass it at each step during a rollout
params=make_composite_from_td(td_params["params"]),
shape=(),
)
# since the environment is stateless, we expect the previous output as input.
# For this, ``EnvBase`` expects some state_spec to be available
self.state_spec = self.observation_spec.clone()
# action-spec will be automatically wrapped in input_spec when
# `self.action_spec = spec` will be called supported
self.action_spec = Bounded(
low=-td_params["params", "max_torque"],
high=td_params["params", "max_torque"],
shape=(1,),
dtype=torch.float32,
)
self.reward_spec = Unbounded(shape=(*td_params.shape, 1))
def make_composite_from_td(td):
# custom function to convert a ``tensordict`` in a similar spec structure
# of unbounded values.
composite = Composite(
{
key: make_composite_from_td(tensor)
if isinstance(tensor, TensorDictBase)
else Unbounded(dtype=tensor.dtype, device=tensor.device, shape=tensor.shape)
for key, tensor in td.items()
},
shape=td.shape,
)
return composite
可重现的实验：播种¶
播种环境是初始化实验时的常见操作。EnvBase._set_seed() 的唯一目标是设置包含的模拟器的种子。如果可能，此操作不应调用 reset() 或与环境执行交互。父 EnvBase.set_seed() 方法包含一种机制，该机制允许使用不同的伪随机且可重现的种子播种多个环境。
def _set_seed(self, seed: Optional[int]):
rng = torch.manual_seed(seed)
self.rng = rng
将所有内容整合在一起：EnvBase 类¶
我们终于可以将各个部分放在一起并设计我们的环境类。规范初始化需要在环境构建期间执行，因此我们必须注意在 PendulumEnv.__init__() 中调用 _make_spec() 方法。
我们添加一个静态方法 PendulumEnv.gen_params()，它确定性地生成一组在执行期间要使用的超参数
def gen_params(g=10.0, batch_size=None) -> TensorDictBase:
"""Returns a ``tensordict`` containing the physical parameters such as gravitational force and torque or speed limits."""
if batch_size is None:
batch_size = []
td = TensorDict(
{
"params": TensorDict(
{
"max_speed": 8,
"max_torque": 2.0,
"dt": 0.05,
"g": g,
"m": 1.0,
"l": 1.0,
},
[],
)
},
[],
)
if batch_size:
td = td.expand(batch_size).contiguous()
return td
我们将环境定义为非 batch_locked，方法是将 同名 属性设置为 False。这意味着我们不会强制输入 tensordict 具有与环境批量大小匹配的 batch-size。
以下代码将只是将我们上面编写的各个部分放在一起。
class PendulumEnv(EnvBase):
metadata = {
"render_modes": ["human", "rgb_array"],
"render_fps": 30,
}
batch_locked = False
def __init__(self, td_params=None, seed=None, device="cpu"):
if td_params is None:
td_params = self.gen_params()
super().__init__(device=device, batch_size=[])
self._make_spec(td_params)
if seed is None:
seed = torch.empty((), dtype=torch.int64).random_().item()
self.set_seed(seed)
# Helpers: _make_step and gen_params
gen_params = staticmethod(gen_params)
_make_spec = _make_spec
# Mandatory methods: _step, _reset and _set_seed
_reset = _reset
_step = staticmethod(_step)
_set_seed = _set_seed
测试我们的环境¶
TorchRL 提供了一个简单的函数 check_env_specs()，用于检查（转换后的）环境是否具有与其规范所指示的输入/输出结构相匹配的结构。让我们试用一下
env = PendulumEnv()
check_env_specs(env)
我们可以查看我们的规范，以直观地表示环境签名
print("observation_spec:", env.observation_spec)
print("state_spec:", env.state_spec)
print("reward_spec:", env.reward_spec)
observation_spec: Composite(
th: BoundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
thdot: BoundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
params: Composite(
max_speed: UnboundedDiscrete(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, contiguous=True)),
device=cpu,
dtype=torch.int64,
domain=discrete),
max_torque: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
dt: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
g: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
m: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
l: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
device=cpu,
shape=torch.Size([])),
device=cpu,
shape=torch.Size([]))
state_spec: Composite(
th: BoundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
thdot: BoundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
params: Composite(
max_speed: UnboundedDiscrete(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, contiguous=True)),
device=cpu,
dtype=torch.int64,
domain=discrete),
max_torque: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
dt: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
g: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
m: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
l: UnboundedContinuous(
shape=torch.Size([]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous),
device=cpu,
shape=torch.Size([])),
device=cpu,
shape=torch.Size([]))
reward_spec: UnboundedContinuous(
shape=torch.Size([1]),
space=ContinuousBox(
low=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True),
high=Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.float32, contiguous=True)),
device=cpu,
dtype=torch.float32,
domain=continuous)
我们也可以执行几个命令来检查输出结构是否与预期匹配。
td = env.reset()
print("reset tensordict", td)
reset tensordict TensorDict(
fields={
done: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=None,
is_shared=False),
terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
th: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=None,
is_shared=False)
我们可以运行 env.rand_step() 以从 action_spec 域中随机生成一个动作。由于我们的环境是无状态的，因此必须传递包含超参数和当前状态的 tensordict。在有状态上下文中，env.rand_step() 也完美运行。
td = env.rand_step(td)
print("random step tensordict", td)
random 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),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=None,
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),
th: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=None,
is_shared=False),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=None,
is_shared=False),
terminated: Tensor(shape=torch.Size([1]), device=cpu, dtype=torch.bool, is_shared=False),
th: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([]),
device=None,
is_shared=False)
转换环境¶
为无状态模拟器编写环境变换比为有状态模拟器编写环境变换稍微复杂一些：转换需要在下一次迭代时读取的输出条目需要在下一步调用 meth.step() 之前应用逆变换。这是展示 TorchRL 变换所有功能的理想场景！
例如，在以下转换后的环境中，我们 unsqueeze 条目 ["th", "thdot"]，以便能够沿最后一个维度堆叠它们。我们还将它们作为 in_keys_inv 传递，以便在将它们作为输入传递到下一次迭代时将其挤回到其原始形状。
env = TransformedEnv(
env,
# ``Unsqueeze`` the observations that we will concatenate
UnsqueezeTransform(
dim=-1,
in_keys=["th", "thdot"],
in_keys_inv=["th", "thdot"],
),
)
编写自定义变换¶
TorchRL 的变换可能无法涵盖在环境执行后想要执行的所有操作。编写变换不需要太多努力。与环境设计一样，编写变换也有两个步骤
使动态正确（正向和逆向）；
调整环境规范。
变换可以在两种设置中使用：它本身可以用作 Module。它也可以附加到 TransformedEnv。类的结构允许自定义不同上下文中的行为。
Transform 骨架可以总结如下
class Transform(nn.Module):
def forward(self, tensordict):
...
def _apply_transform(self, tensordict):
...
def _step(self, tensordict):
...
def _call(self, tensordict):
...
def inv(self, tensordict):
...
def _inv_apply_transform(self, tensordict):
...
有三个入口点（forward()、_step() 和 inv()），它们都接收 tensordict.TensorDict 实例。前两个最终将遍历由 in_keys 指示的键，并对每个键调用 _apply_transform()。结果将写入由 Transform.out_keys 指示的条目中（如果未提供，则 in_keys 将使用转换后的值进行更新）。如果需要执行逆变换，则将执行类似的数据流，但使用 Transform.inv() 和 Transform._inv_apply_transform() 方法，并跨 in_keys_inv 和 out_keys_inv 键列表。下图总结了环境和回放缓冲区的此流程。
变换 API
在某些情况下，变换不会以单一方式对键的子集起作用，而是会对父环境执行某些操作或处理整个输入 tensordict。在这些情况下，应重写 _call() 和 forward() 方法，并且可以跳过 _apply_transform() 方法。
让我们编写新的变换，计算位置角度的 sine 和 cosine 值，因为这些值比原始角度值更有助于我们学习策略
class SinTransform(Transform):
def _apply_transform(self, obs: torch.Tensor) -> None:
return obs.sin()
# The transform must also modify the data at reset time
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
return self._call(tensordict_reset)
# _apply_to_composite will execute the observation spec transform across all
# in_keys/out_keys pairs and write the result in the observation_spec which
# is of type ``Composite``
@_apply_to_composite
def transform_observation_spec(self, observation_spec):
return Bounded(
low=-1,
high=1,
shape=observation_spec.shape,
dtype=observation_spec.dtype,
device=observation_spec.device,
)
class CosTransform(Transform):
def _apply_transform(self, obs: torch.Tensor) -> None:
return obs.cos()
# The transform must also modify the data at reset time
def _reset(
self, tensordict: TensorDictBase, tensordict_reset: TensorDictBase
) -> TensorDictBase:
return self._call(tensordict_reset)
# _apply_to_composite will execute the observation spec transform across all
# in_keys/out_keys pairs and write the result in the observation_spec which
# is of type ``Composite``
@_apply_to_composite
def transform_observation_spec(self, observation_spec):
return Bounded(
low=-1,
high=1,
shape=observation_spec.shape,
dtype=observation_spec.dtype,
device=observation_spec.device,
)
t_sin = SinTransform(in_keys=["th"], out_keys=["sin"])
t_cos = CosTransform(in_keys=["th"], out_keys=["cos"])
env.append_transform(t_sin)
env.append_transform(t_cos)
TransformedEnv(
env=PendulumEnv(),
transform=Compose(
UnsqueezeTransform(dim=-1, in_keys=['th', 'thdot'], out_keys=['th', 'thdot'], in_keys_inv=['th', 'thdot'], out_keys_inv=['th', 'thdot']),
SinTransform(keys=['th']),
CosTransform(keys=['th'])))
将观察结果连接到“observation”条目上。del_keys=False 确保我们为下一次迭代保留这些值。
cat_transform = CatTensors(
in_keys=["sin", "cos", "thdot"], dim=-1, out_key="observation", del_keys=False
)
env.append_transform(cat_transform)
TransformedEnv(
env=PendulumEnv(),
transform=Compose(
UnsqueezeTransform(dim=-1, in_keys=['th', 'thdot'], out_keys=['th', 'thdot'], in_keys_inv=['th', 'thdot'], out_keys_inv=['th', 'thdot']),
SinTransform(keys=['th']),
CosTransform(keys=['th']),
CatTensors(in_keys=['cos', 'sin', 'thdot'], out_key=observation)))
再一次，让我们检查一下我们的环境规范是否与接收到的内容匹配
check_env_specs(env)
执行 rollout¶
执行 rollout 是一系列简单的步骤
重置环境
当未满足某些条件时
计算给定策略的动作
执行给定此动作的步进
收集数据
进行
MDP步进
收集数据并返回
这些操作已方便地包装在 rollout() 方法中，我们在此处提供了一个简化版本。
def simple_rollout(steps=100):
# preallocate:
data = TensorDict({}, [steps])
# reset
_data = env.reset()
for i in range(steps):
_data["action"] = env.action_spec.rand()
_data = env.step(_data)
data[i] = _data
_data = step_mdp(_data, keep_other=True)
return data
print("data from rollout:", simple_rollout(100))
data from rollout: TensorDict(
fields={
action: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
cos: 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={
cos: 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),
observation: Tensor(shape=torch.Size([100, 3]), device=cpu, dtype=torch.float32, is_shared=False),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([100]),
device=None,
is_shared=False),
reward: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
sin: 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),
th: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, 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),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([100]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([100]),
device=None,
is_shared=False),
sin: 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),
th: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([100, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([100]),
device=None,
is_shared=False)
批量计算¶
本教程中最后一个未探索的结尾是我们有能力在 TorchRL 中批量计算。由于我们的环境对输入数据形状没有任何假设，因此我们可以无缝地在批处理数据上执行它。更好的是：对于非批量锁定环境（例如我们的 Pendulum），我们可以动态更改批量大小，而无需重新创建环境。为此，我们只需生成具有所需形状的参数。
batch_size = 10 # number of environments to be executed in batch
td = env.reset(env.gen_params(batch_size=[batch_size]))
print("reset (batch size of 10)", td)
td = env.rand_step(td)
print("rand step (batch size of 10)", td)
reset (batch size of 10) TensorDict(
fields={
cos: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
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),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10]),
device=None,
is_shared=False),
sin: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
th: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10]),
device=None,
is_shared=False)
rand step (batch size of 10) TensorDict(
fields={
action: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
cos: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
cos: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
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),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10]),
device=None,
is_shared=False),
reward: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
sin: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
th: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10]),
device=None,
is_shared=False),
observation: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10]),
device=None,
is_shared=False),
sin: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.bool, is_shared=False),
th: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([10, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10]),
device=None,
is_shared=False)
使用批量数据执行 rollout 需要我们在 rollout 函数外部重置环境，因为我们需要动态定义 batch_size，而 rollout() 不支持这样做
rollout = env.rollout(
3,
auto_reset=False, # we're executing the reset out of the ``rollout`` call
tensordict=env.reset(env.gen_params(batch_size=[batch_size])),
)
print("rollout of len 3 (batch size of 10):", rollout)
rollout of len 3 (batch size of 10): TensorDict(
fields={
action: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
cos: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
next: TensorDict(
fields={
cos: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
observation: Tensor(shape=torch.Size([10, 3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10, 3]),
device=None,
is_shared=False),
reward: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
sin: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
th: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10, 3]),
device=None,
is_shared=False),
observation: Tensor(shape=torch.Size([10, 3, 3]), device=cpu, dtype=torch.float32, is_shared=False),
params: TensorDict(
fields={
dt: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
g: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
l: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
m: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False),
max_speed: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.int64, is_shared=False),
max_torque: Tensor(shape=torch.Size([10, 3]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10, 3]),
device=None,
is_shared=False),
sin: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.bool, is_shared=False),
th: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False),
thdot: Tensor(shape=torch.Size([10, 3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
batch_size=torch.Size([10, 3]),
device=None,
is_shared=False)
训练简单策略¶
在本示例中，我们将使用奖励作为可微分目标（例如负损失）来训练一个简单的策略。我们将利用我们的动态系统完全可微分的事实，通过轨迹回报进行反向传播，并调整策略的权重以直接最大化此值。当然，在许多设置中，我们做出的许多假设都不成立，例如可微分系统和完全访问底层机制。
尽管如此，这是一个非常简单的示例，展示了如何使用 TorchRL 中的自定义环境编写训练循环。
让我们首先编写策略网络
torch.manual_seed(0)
env.set_seed(0)
net = nn.Sequential(
nn.LazyLinear(64),
nn.Tanh(),
nn.LazyLinear(64),
nn.Tanh(),
nn.LazyLinear(64),
nn.Tanh(),
nn.LazyLinear(1),
)
policy = TensorDictModule(
net,
in_keys=["observation"],
out_keys=["action"],
)
以及我们的优化器
optim = torch.optim.Adam(policy.parameters(), lr=2e-3)
训练循环¶
我们将依次执行
生成轨迹
对奖励求和
通过由这些操作定义的图进行反向传播
裁剪梯度范数并执行优化步骤
重复
在训练循环结束时，我们应该得到一个接近于 0 的最终奖励，这表明摆锤如期望的那样向上且静止。
batch_size = 32
pbar = tqdm.tqdm(range(20_000 // batch_size))
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, 20_000)
logs = defaultdict(list)
for _ in pbar:
init_td = env.reset(env.gen_params(batch_size=[batch_size]))
rollout = env.rollout(100, policy, tensordict=init_td, auto_reset=False)
traj_return = rollout["next", "reward"].mean()
(-traj_return).backward()
gn = torch.nn.utils.clip_grad_norm_(net.parameters(), 1.0)
optim.step()
optim.zero_grad()
pbar.set_description(
f"reward: {traj_return: 4.4f}, "
f"last reward: {rollout[..., -1]['next', 'reward'].mean(): 4.4f}, gradient norm: {gn: 4.4}"
)
logs["return"].append(traj_return.item())
logs["last_reward"].append(rollout[..., -1]["next", "reward"].mean().item())
scheduler.step()
def plot():
import matplotlib
from matplotlib import pyplot as plt
is_ipython = "inline" in matplotlib.get_backend()
if is_ipython:
from IPython import display
with plt.ion():
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.plot(logs["return"])
plt.title("returns")
plt.xlabel("iteration")
plt.subplot(1, 2, 2)
plt.plot(logs["last_reward"])
plt.title("last reward")
plt.xlabel("iteration")
if is_ipython:
display.display(plt.gcf())
display.clear_output(wait=True)
plt.show()
plot()
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结论¶
在本教程中，我们学习了如何从头开始编写一个无状态环境。我们涉及了以下主题：
在编写环境时需要注意的四个基本组件（
step、reset、播种和构建规范）。我们看到了这些方法和类如何与TensorDict类交互；如何使用
check_env_specs()测试环境是否被正确编码；如何在无状态环境的上下文中附加转换，以及如何编写自定义转换；
如何在完全可微分的模拟器上训练策略。
脚本总运行时间： (2 分钟 51.454 秒)
估计内存使用量： 320 MB
