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钟摆：使用 TorchRL 编写环境和转换#
创建日期：2023年11月9日 | 最后更新：2025年1月27日 | 最后验证：2024年11月5日
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中不可或缺的一部分。
TorchRL 提供了一套工具，可在多种场景下实现这一目标。本教程演示了如何从零开始使用 PyTorch 和 TorchRL 编写一个钟摆模拟器。其灵感来源于 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：- 编写规格说明（输入、观测和奖励）；- 实现行为：播种（seeding）、重置（reset）和步进（step）。
转换环境的输入和输出，并编写您自己的转换（transforms）；
如何使用
TensorDict在codebase中传递任意数据结构。在此过程中，我们将接触 TorchRL 的三个关键组件
为了让您了解 TorchRL 环境所能实现的功能，我们将设计一个无状态环境。虽然有状态环境会跟踪遇到的最新物理状态并依赖此状态来模拟状态到状态的转换，但无状态环境期望在每一步中将当前状态与采取的动作一起提供给它。TorchRL 同时支持这两种类型的环境，但无状态环境更为通用，因此涵盖了 TorchRL 环境 API 中更广泛的功能。
对无状态环境建模，用户可以完全控制模拟器的输入和输出：可以在任何阶段重置实验，或从外部主动修改动力学。然而，这假设我们对任务有一定的控制权，但情况并非总是如此：解决无法控制当前状态的问题更具挑战性，但应用范围要广泛得多。
无状态环境的另一个优势是它们可以支持转换模拟的批处理执行。如果后端和实现允许，代数运算可以在标量、向量或张量上无缝执行。本教程将提供此类示例。
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()）以及最后的规格说明（specs）。编写完模拟器后，我们将演示如何在训练过程中结合转换使用它。
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。
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 BoundedTensorSpec, CompositeSpec, UnboundedContinuousTensorSpec
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
在设计新的环境类时，有四件事必须处理
EnvBase._reset()，用于对模拟器在（可能是随机的）初始状态下进行重置；EnvBase._step()，用于对状态转换动态进行编码；EnvBase._set_seed`()，用于实现播种机制；环境规格说明。
首先描述我们要处理的问题：我们想要模拟一个简单的钟摆，并控制作用在其固定点上的扭矩。我们的目标是将钟摆放置在向上位置（按惯例角度位置为 0），并使其在该位置保持静止。为了设计动力系统，我们需要定义两个方程：动作（应用的扭矩）后的运动方程，以及构成我们目标函数的奖励方程。
对于运动方程，我们将按以下方式更新角速度
\[\dot{\theta}_{t+1} = \dot{\theta}_t + (3 * g / (2 * L) * \sin(\theta_t) + 3 / (m * L^2) * u) * dt\]
其中 \(\dot{\theta}\) 是以弧度/秒为单位的角速度，\(g\) 是重力加速度，\(L\) 是钟摆长度，\(m\) 是质量，\(\theta\) 是角位置，\(u\) 是扭矩。角位置随后根据以下公式更新：
\[\theta_{t+1} = \theta_{t} + \dot{\theta}_{t+1} dt\]
我们定义奖励为
\[r = -(\theta^2 + 0.1 * \dot{\theta}^2 + 0.001 * u^2)\]
当角度接近 0（钟摆向上）、角速度接近 0（无运动）且扭矩也为 0 时，奖励将被最大化。
编码动作的效果：_step()#
Step 方法是首先要考虑的，因为它将编码我们感兴趣的模拟。在 TorchRL 中，EnvBase 类具有一个 EnvBase.step() 方法，它接收一个包含 "action" 条目的 tensordict.TensorDict 实例，指示要采取什么动作。
为了便于从该 tensordict 读取和写入，并确保键与库的预期一致，模拟部分被委托给了一个私有抽象方法 _step()，它从 tensordict 读取输入数据，并写入一个带有输出数据的新 tensordict。
_step() 方法应该执行以下操作
读取输入键（例如
"action"）并基于此执行模拟；检索观测值、完成状态和奖励；
将观测值集连同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，这看起来像这样
>>> 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)
注意，根 tensordict 没有改变，唯一的修改是出现了一个包含新信息的新的 "next" 条目。
在钟摆示例中，我们的 _step() 方法将从输入 tensordict 读取相关条目，并计算在应用 "action" 键编码的力之后钟摆的位置和速度。我们将钟摆的“新位置” "new_th" 计算为之前位置 "th" 加上在时间间隔 dt 内的新速度 "new_thdot" 的结果。
由于我们的目标是将钟摆向上转动并保持在该位置静止，我们的 cost（负奖励）函数对于接近目标的角度和低速位置而言更低。事实上，我们要抑制那些远离“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()#
我们需要关心的第二个方法是 _reset() 方法。像 _step() 一样，它应该在其输出的 tensordict 中写入观测条目和可能的完成状态（如果省略了完成状态，它将由父方法 reset() 填充为 False）。在某些上下文中，要求 _reset 方法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被重置）。这就是为什么 _reset() 方法也需要一个 tensordict 作为输入，尽管它完全可以是空的或 None。
父级 EnvBase.reset() 会做一些简单的检查，就像 EnvBase.step() 所做的那样，例如确保输出 tensordict 中返回了 "done" 状态，并且形状与规格所预期的相匹配。
对我们来说，唯一重要的事情是考虑 EnvBase._reset() 是否包含了所有预期的观测值。再一次，由于我们是在无状态环境中工作，我们将钟摆的配置传递给一个名为 "params" 的嵌套 tensordict。
在此示例中，我们不传递完成状态，因为这对于 _reset() 不是强制性的，并且我们的环境是非终止的，所以我们总是期望它是 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#
规格定义了环境的输入和输出域。准确定义运行时将接收到的张量非常重要，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，对于现实世界的物理系统，这可能代价高昂）。
我们的环境必须编码四个规格
EnvBase.observation_spec：这将是一个CompositeSpec实例，其中每个键都是一个观测值（CompositeSpec可以被视为规格字典）。EnvBase.action_spec：它可以是任何类型的规格，但要求它对应于输入tensordict中的"action"条目；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 规格分为两个通用容器：input_spec（包含 step 函数读取的信息规格，分为 action_spec 和 state_spec）和 output_spec（编码 step 输出的规格，即 observation_spec、reward_spec 和 done_spec）。通常，不应直接与 output_spec 和 input_spec 交互，而应仅与它们的内容交互：observation_spec、reward_spec、done_spec、action_spec 和 state_spec。原因是规格在 output_spec 和 input_spec 中以非平凡的方式组织，这两者都不应直接修改。
换句话说，observation_spec 和相关属性是访问输出和输入规格容器内容的便捷快捷方式。
TorchRL 提供了多个 环境规格的前导维度必须与环境的 batch-size 相匹配。这样做是为了确保环境的每个组件（包括其转换）都对预期的输入和输出形状有准确的表示。这是在有状态设置中应该准确编码的内容。 对于非批锁定的环境，例如我们示例中的环境（见下文），这无关紧要，因为环境的批大小很可能是空的。TensorSpec 子类 来编码环境的输入和输出特征。
规格形状#
def _make_spec(self, td_params):
# Under the hood, this will populate self.output_spec["observation"]
self.observation_spec = CompositeSpec(
th=BoundedTensorSpec(
low=-torch.pi,
high=torch.pi,
shape=(),
dtype=torch.float32,
),
thdot=BoundedTensorSpec(
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 = BoundedTensorSpec(
low=-td_params["params", "max_torque"],
high=td_params["params", "max_torque"],
shape=(1,),
dtype=torch.float32,
)
self.reward_spec = UnboundedContinuousTensorSpec(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 = CompositeSpec(
{
key: make_composite_from_td(tensor)
if isinstance(tensor, TensorDictBase)
else UnboundedContinuousTensorSpec(
dtype=tensor.dtype, device=tensor.device, shape=tensor.shape
)
for key, tensor in td.items()
},
shape=td.shape,
)
return composite
可复现的实验：播种#
在初始化实验时，播种环境是一种常见的操作。EnvBase._set_seed() 的唯一目标是设置所包含模拟器的种子。如果可能，此操作不应调用 reset() 或与环境执行交互。父级 EnvBase.set_seed() 方法包含一种机制，允许使用不同的伪随机和可复现种子来播种多个环境。
def _set_seed(self, seed: Optional[int]):
rng = torch.manual_seed(seed)
self.rng = rng
综合各部分：EnvBase 类#
我们终于可以把碎片拼凑起来并设计我们的环境类了。规格初始化需要在环境构造期间执行，因此我们必须在 PendulumEnv.__init__() 中调用 _make_spec() 方法。
我们添加了一个静态方法 PendulumEnv.gen_params()，它确定性地生成一组要在执行期间使用的超参数。
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，通过将 homonymous 属性设为 False。这意味着我们不会强制要求输入 tensordict 具有与环境匹配的 batch-size。
下面的代码将我们上面编码的碎片组合在一起。
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() 来检查（已转换的）环境是否具有符合其规格要求的输入/输出结构。让我们试一试。
env = PendulumEnv()
check_env_specs(env)
/usr/local/lib/python3.10/dist-packages/torchrl/data/tensor_specs.py:7085: DeprecationWarning: The BoundedTensorSpec has been deprecated and will be removed in v0.8. Please use Bounded instead.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/torchrl/data/tensor_specs.py:7085: DeprecationWarning: The UnboundedContinuousTensorSpec has been deprecated and will be removed in v0.8. Please use Unbounded instead.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/torchrl/data/tensor_specs.py:7085: DeprecationWarning: The CompositeSpec has been deprecated and will be removed in v0.8. Please use Composite instead.
warnings.warn(
2026-07-08 23:35:02,962 [torchrl][INFO] check_env_specs succeeded! [END]
我们可以查看我们的规格，以获得环境签名的可视化表示。
print("observation_spec:", env.observation_spec)
print("state_spec:", env.state_spec)
print("reward_spec:", env.reward_spec)
observation_spec: CompositeSpec(
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: CompositeSpec(
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([]),
data_cls=None),
device=cpu,
shape=torch.Size([]),
data_cls=None)
state_spec: CompositeSpec(
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: CompositeSpec(
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([]),
data_cls=None),
device=cpu,
shape=torch.Size([]),
data_cls=None)
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相符。
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() 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，必须传递一个包含超参数和当前状态的 tensordict。在有状态的上下文中，env.rand_step() 也能完美工作。
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转换比为有状态的要复杂一些：转换需要在下一次迭代中读取的输出条目，需要在下次迭代中调用 meth.step() 之前应用逆转换。这是一个展示 TorchRL 转换所有功能的理想场景！
例如，在下面的转换环境中，我们 unsqueeze 了条目 ["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，以便在下次迭代中作为输入传递时将它们挤压回原始形状。
env = TransformedEnv(
env,
# ``Unsqueeze`` the observations that we will concatenate
UnsqueezeTransform(
dim=-1,
in_keys=["th", "thdot"],
in_keys_inv=["th", "thdot"],
),
)
编写自定义转换#
TorchRL 的转换可能无法涵盖在执行环境后人们想要执行的所有操作。编写一个转换不需要太多的努力。与环境设计一样，编写转换有两个步骤
正确处理动力学（前向和逆向）；
调整环境规格。
转换可以在两种设置中使用：单独使用时，它可以作为 Module 使用。它也可以附加到 TransformedEnv。类结构允许在不同上下文中自定义行为。
Transform 骨架可概括如下
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()。如果提供了 Transform.out_keys，结果将被写入其中指向的条目（如果不提供，in_keys 将使用转换后的值进行更新）。如果需要执行逆转换，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 键列表进行。下图总结了环境和重放缓冲区的这种流向。
转换 API
在某些情况下，转换不会以统一的方式对键的子集进行操作，而是会在父环境上执行某些操作，或者使用整个输入 tensordict。在这些情况下，应该重写 _call() 和 forward() 方法，并且可以跳过 _apply_transform() 方法。
让我们编写新的转换，计算位置角的 sine 和 cosine 值，因为这些值比原始角度值对我们学习策略更有用。
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 BoundedTensorSpec(
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 BoundedTensorSpec(
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'])))
将观测值连接到“观测”条目中。del_keys=False 确保我们保留这些值以用于下一次迭代。
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，让我们检查我们的环境规格是否与收到的相匹配。
2026-07-08 23:35:02,997 [torchrl][INFO] check_env_specs succeeded! [END]
执行 rollout#
执行 rollout 是一系列简单的步骤
重置环境
当某些条件未满足时
给定策略计算动作
给定此动作执行一步
收集数据
执行一步
MDP
收集数据并返回
这些操作已方便地封装在 rollout() 方法中，我们在下面提供了其简化版本。
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)
批处理计算#
我们教程中最后一个未探索的部分是我们可以在 TorchRL 中批处理计算的能力。因为我们的环境不对输入数据形状做任何假设，所以我们可以在数据批次上无缝执行它。更好的是：对于像我们的钟摆这样的非批锁定环境，我们可以即时更改批大小，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。
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)
用一批数据执行 rollout 需要我们在 rollout 函数之外重置环境，因为我们需要动态定义 batch_size，而 rollout() 不支持这一点。
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)
训练一个简单的策略#
在此示例中，我们将使用奖励作为可微目标来训练一个简单的策略，例如作为负损失。我们将利用我们的动力系统完全可微的事实，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该值。当然，在许多设置中，我们所做的许多假设并不成立，例如可微系统和对底层机制的完全访问。
尽管如此，这是一个非常简单的示例，展示了如何在 TorchRL 中使用自定义环境编写训练循环。
让我们先写策略网络
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
生成一条轨迹
汇总奖励
通过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，我们应该得到接近 0 的最终奖励，这表明钟摆按预期向上且静止。
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，我们学习了如何从零开始编写无状态环境。我们触及了以下主题：
编写环境时需要处理的四个基本组件（
step、reset、播种和构建规格）。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()测试环境是否编写正确；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；
如何在完全可微的模拟器上训练策略。
脚本总运行时间：（2 分 9.581 秒）