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Pendulum：使用 TorchRL 编写环境和转换¶
创建于：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的组成部分。
TorchRL 提供了一套工具，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 Pendulum 模拟器代码。它自由地借鉴了 OpenAI-Gym/Farama-Gymnasium 控制库 中的 Pendulum-v1 实现。
简易 Pendulum¶
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，并编写您自己的转换；
如何使用
TensorDict在整个代码库中携带任意数据结构。在此过程中，我们将接触 TorchRL 的三个关键组件
为了让您了解使用 TorchRL 环境可以实现的目标，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它们提供当前状态以及所采取的动作。TorchRL 同时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 中环境 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：可以在任何阶段重置实验，或从外部主动修改动态。但是，它假设我们可以控制任务，但这可能并非总是如此：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范围。
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，则可以无缝地对标量、向量或张量执行代数运算。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。在编写模拟器代码之后，我们将演示如何在训练期间使用转换来使用它。
我们将探索 TorchRL 的 API 带来的新途径，包括：转换输入的可能性、模拟的矢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，您必须注意四件事
EnvBase._reset()，它为模拟器在（可能随机的）初始状态下的重置编码；EnvBase._step()，它为状态转换动态编码；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 Pendulum，我们可以控制施加在其固定点上的扭矩。我们的目标是将 Pendulum 放置在向上位置（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：动作后的运动方程（施加的扭矩）和构成我们目标函数的奖励方程。
对于运动方程，我们将更新角速度，如下所示
\[\dot{\theta}_{t+1} = \dot{\theta}_t + (3 * g / (2 * L) * \sin(\theta_t) + 3 / (m * L^2) * u) * dt\]
其中 \(\dot{\theta}\) 是角速度，单位为 rad/sec，\(g\) 是重力，\(L\) 是 Pendulum 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（Pendulum 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()¶
步进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 类具有 EnvBase.step() 方法，该方法接收 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 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，并写入带有输出数据新 tensordict。
_step() 方法应执行以下操作
读取输入键（例如
"action"）并根据这些键执行模拟；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中相应条目。
接下来，step() 方法将 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，这将如下所示
>>> 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 读取相关条目，并计算在将由 "action" 键编码的力施加到 Pendulum 上之后，Pendulum 的位置和速度。我们将 Pendulum 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 Pendulum 向上转动并使其在该位置保持静止，因此对于接近目标位置和低速的位置，我们的 成本（负奖励）函数较低。
实际上，我们希望不鼓励远离“向上”的位置和/或远离 0 的速度。
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
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。
重置模拟器：_reset()¶
我们需要关注的第二个方法是 _reset() 方法。与 _step() 一样，它应该在它输出的 tensordict 中写入观察条目和可能的完成状态（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，需要 _reset 方法接收来自调用它的函数的命令（例如，在多代理设置中，我们可能想要指示哪些代理需要重置）。这就是为什么 _reset() 方法也期望 tensordict 作为输入，尽管它完全可以是空的或 None。
父 EnvBase.reset() 执行一些简单的检查，例如 EnvBase.step() 执行的检查，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，唯一需要考虑的重要事项是 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，因此我们将 Pendulum 的配置传递到名为 "params" 的嵌套 tensordict 中。
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
在本示例中，我们不传递完成状态，因为这不是 _reset() 的强制要求，并且我们的环境是非终止的，因此我们始终期望它为 False。
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，规范准确地定义了在运行时将接收到的张量，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，这可能代价高昂）。
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可以视为规范字典）。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，其中包含步进函数读取的信息的规范（在包含动作的 action_spec 和包含其余部分的 state_spec 之间划分），以及 output_spec，其中编码步进输出的规范（observation_spec、reward_spec 和 done_spec）。通常，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。原因是规范以非平凡的方式组织在 output_spec 和 input_spec 中，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，用于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。这样做是为了强制执行环境的每个组件（包括其转换）都准确表示预期的输入和输出形状。这是在有状态设置中应准确编码的内容。
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
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。
可重现的实验：播种¶
def _set_seed(self, seed: Optional[int]):
rng = torch.manual_seed(seed)
self.rng = rng
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来播种多个环境。
将事物整合在一起：EnvBase 类¶
我们终于可以将各个部分组合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。
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
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 的 批量大小 与环境的批量大小相匹配。
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
以下代码将仅将我们上面编写的各个部分放在一起。
测试我们的环境¶
env = PendulumEnv()
check_env_specs(env)
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指示的输入/输出结构相匹配的结构。让我们试用一下
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)
我们可以查看我们的规范，以直观地表示环境签名
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，必须传入包含超参数和当前状态的 tensordict。在有状态的上下文中，env.rand_step() 也同样可以完美运行。
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转换比为有状态模拟器编写环境转换稍微复杂：转换需要在下一次迭代中读取的输出条目，需要在下一步调用 meth.step() 之前应用逆转换。这是展示 TorchRL 转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，以便在它们作为输入在下一次迭代中传递时，将它们挤回原始形状。
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环境执行后想要执行的所有操作。编写转换不需要太多 effort。至于环境设计，编写转换有两个步骤
正确获取动态（前向和逆向）；
调整环境规范。
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 上使用。该类的结构允许自定义不同上下文中的行为。
一个 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 in_keys 指示的键，并对每个键调用 _apply_transform()。结果将写入 Transform.out_keys 指向的条目中（如果未提供，则 in_keys 将使用转换后的值进行更新）。如果需要执行逆转换，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这种流程。
转换 API
在某些情况下，转换不会以统一的方式处理键的子集，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。
让我们编写新的转换，以计算位置角的 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 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 “observation” 条目。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)))
再次，让我们检查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
当某些条件不满足时
根据策略计算动作
执行此动作的步骤
收集数据
进行
MDP步骤
收集数据并返回
这些操作已方便地包装在 rollout() 方法中，我们在此处提供一个简化的版本如下。
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¶
我们教程中最后一个未探索的方面是我们在 TorchRL 中批量计算的能力。由于我们的环境对输入数据形状没有任何假设，我们可以无缝地在数据批次上执行它。更棒的是：对于非批次锁定的环境（例如我们的 Pendulum），我们可以动态更改批次大小，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即可。
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() 不支持此操作
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¶
在本示例中，我们将使用奖励作为可微分目标来训练一个简单的策略，例如负损失。我们将利用我们的动态系统完全可微分的事实，通过轨迹返回进行反向传播，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并不成立，例如可微分系统和对底层机制的完全访问权限。
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
生成轨迹
求和奖励
通过这些操作定义的图进行反向传播
裁剪梯度范数并进行优化步骤
重复
在训练循环结束时，我们应该有一个接近于 0 的最终奖励，这表明摆锤是向上且静止的，正如预期的那样。
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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reward: -4.4170, last reward: -3.4675, gradient norm: 60.04: 97%|#########6| 605/625 [01:39<00:03, 6.14it/s]
reward: -4.4170, last reward: -3.4675, gradient norm: 60.04: 97%|#########6| 606/625 [01:39<00:03, 6.15it/s]
reward: -4.3153, last reward: -2.9316, gradient norm: 53.11: 97%|#########6| 606/625 [01:39<00:03, 6.15it/s]
reward: -4.3153, last reward: -2.9316, gradient norm: 53.11: 97%|#########7| 607/625 [01:39<00:02, 6.15it/s]
reward: -3.0649, last reward: -0.9722, gradient norm: 30.84: 97%|#########7| 607/625 [01:39<00:02, 6.15it/s]
reward: -3.0649, last reward: -0.9722, gradient norm: 30.84: 97%|#########7| 608/625 [01:39<00:02, 6.14it/s]
reward: -2.7989, last reward: -0.0329, gradient norm: 1.261: 97%|#########7| 608/625 [01:39<00:02, 6.14it/s]
reward: -2.7989, last reward: -0.0329, gradient norm: 1.261: 97%|#########7| 609/625 [01:39<00:02, 6.15it/s]
reward: -2.1976, last reward: -0.6852, gradient norm: 20.33: 97%|#########7| 609/625 [01:39<00:02, 6.15it/s]
reward: -2.1976, last reward: -0.6852, gradient norm: 20.33: 98%|#########7| 610/625 [01:39<00:02, 6.15it/s]
reward: -2.4793, last reward: -0.1255, gradient norm: 14.69: 98%|#########7| 610/625 [01:39<00:02, 6.15it/s]
reward: -2.4793, last reward: -0.1255, gradient norm: 14.69: 98%|#########7| 611/625 [01:39<00:02, 6.13it/s]
reward: -2.4581, last reward: -0.0394, gradient norm: 2.429: 98%|#########7| 611/625 [01:40<00:02, 6.13it/s]
reward: -2.4581, last reward: -0.0394, gradient norm: 2.429: 98%|#########7| 612/625 [01:40<00:02, 6.14it/s]
reward: -2.2047, last reward: -0.0326, gradient norm: 1.147: 98%|#########7| 612/625 [01:40<00:02, 6.14it/s]
reward: -2.2047, last reward: -0.0326, gradient norm: 1.147: 98%|#########8| 613/625 [01:40<00:01, 6.15it/s]
reward: -1.8967, last reward: -0.0129, gradient norm: 0.8619: 98%|#########8| 613/625 [01:40<00:01, 6.15it/s]
reward: -1.8967, last reward: -0.0129, gradient norm: 0.8619: 98%|#########8| 614/625 [01:40<00:01, 6.13it/s]
reward: -2.5906, last reward: -0.0015, gradient norm: 0.6491: 98%|#########8| 614/625 [01:40<00:01, 6.13it/s]
reward: -2.5906, last reward: -0.0015, gradient norm: 0.6491: 98%|#########8| 615/625 [01:40<00:01, 6.14it/s]
reward: -1.6634, last reward: -0.0007, gradient norm: 0.4394: 98%|#########8| 615/625 [01:40<00:01, 6.14it/s]
reward: -1.6634, last reward: -0.0007, gradient norm: 0.4394: 99%|#########8| 616/625 [01:40<00:01, 6.13it/s]
reward: -2.0624, last reward: -0.0061, gradient norm: 0.5676: 99%|#########8| 616/625 [01:40<00:01, 6.13it/s]
reward: -2.0624, last reward: -0.0061, gradient norm: 0.5676: 99%|#########8| 617/625 [01:40<00:01, 6.13it/s]
reward: -2.3259, last reward: -0.0131, gradient norm: 0.7733: 99%|#########8| 617/625 [01:41<00:01, 6.13it/s]
reward: -2.3259, last reward: -0.0131, gradient norm: 0.7733: 99%|#########8| 618/625 [01:41<00:01, 6.14it/s]
reward: -1.7515, last reward: -0.0189, gradient norm: 0.5575: 99%|#########8| 618/625 [01:41<00:01, 6.14it/s]
reward: -1.7515, last reward: -0.0189, gradient norm: 0.5575: 99%|#########9| 619/625 [01:41<00:00, 6.15it/s]
reward: -1.9313, last reward: -0.0207, gradient norm: 0.6286: 99%|#########9| 619/625 [01:41<00:00, 6.15it/s]
reward: -1.9313, last reward: -0.0207, gradient norm: 0.6286: 99%|#########9| 620/625 [01:41<00:00, 6.16it/s]
reward: -2.4325, last reward: -0.0171, gradient norm: 0.7832: 99%|#########9| 620/625 [01:41<00:00, 6.16it/s]
reward: -2.4325, last reward: -0.0171, gradient norm: 0.7832: 99%|#########9| 621/625 [01:41<00:00, 6.16it/s]
reward: -2.1134, last reward: -0.0144, gradient norm: 1.96: 99%|#########9| 621/625 [01:41<00:00, 6.16it/s]
reward: -2.1134, last reward: -0.0144, gradient norm: 1.96: 100%|#########9| 622/625 [01:41<00:00, 6.16it/s]
reward: -2.4572, last reward: -0.0500, gradient norm: 0.5838: 100%|#########9| 622/625 [01:41<00:00, 6.16it/s]
reward: -2.4572, last reward: -0.0500, gradient norm: 0.5838: 100%|#########9| 623/625 [01:41<00:00, 6.12it/s]
reward: -2.3818, last reward: -0.0019, gradient norm: 0.8623: 100%|#########9| 623/625 [01:42<00:00, 6.12it/s]
reward: -2.3818, last reward: -0.0019, gradient norm: 0.8623: 100%|#########9| 624/625 [01:42<00:00, 6.10it/s]
reward: -2.1253, last reward: -0.0001, gradient norm: 0.6622: 100%|#########9| 624/625 [01:42<00:00, 6.10it/s]
reward: -2.1253, last reward: -0.0001, gradient norm: 0.6622: 100%|##########| 625/625 [01:42<00:00, 6.07it/s]
reward: -2.1253, last reward: -0.0001, gradient norm: 0.6622: 100%|##########| 625/625 [01:42<00:00, 6.11it/s]
结论¶
在本教程中，我们学习了如何从头开始编写无状态环境。我们涉及了以下主题：
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）。我们了解了这些方法和类如何与TensorDict类交互；如何使用
check_env_specs()测试环境是否已正确编码；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： ( 1 分钟 42.546 秒)
