MultiStepActorWrapper

class torchrl.modules.tensordict_module.MultiStepActorWrapper(*args, **kwargs)

包装多步动作的 Actor。

此类允许在环境中执行宏观操作。Actor 的动作（actions）条目必须有一个额外的时间维度才能被消耗。它必须放置在输入 tensordict 的最后一个维度（即 tensordict.ndim）的旁边。

如果未提供，动作条目键将通过一个简单的启发式方法自动从 Actor 中检索（任何以 "action" 字符串结尾的嵌套键）。

输入 tensordict 中还必须存在一个 "is_init" 条目，用于跟踪当前集合何时应该中断，因为遇到了“完成”状态。与 action_keys 不同，此键必须是唯一的。

参数:

• actor (TensorDictModuleBase) – 一个 Actor。

• n_steps (int, optional) – Actor 一次输出的动作数量（前瞻窗口）。默认为 None。

关键字参数:

• action_keys (list of NestedKeys, optional) – 环境的动作键。可以从 env.action_keys 中检索。默认为 actor 中以 "action" 字符串结尾的所有 out_keys。

• init_key (NestedKey, optional) – 指示环境何时已重置的条目键。默认为 "is_init"，这是 InitTracker 变换的 out_key。

• keep_dim (bool, optional) – 在索引期间是否保留宏观操作的时间维度。默认为 False。

示例

>>> import torch.nn
>>> from torchrl.modules.tensordict_module.actors import MultiStepActorWrapper, Actor
>>> from torchrl.envs import CatFrames, GymEnv, TransformedEnv, SerialEnv, InitTracker, Compose
>>> from tensordict.nn import TensorDictSequential as Seq, TensorDictModule as Mod
>>>
>>> time_steps = 6
>>> n_obs = 4
>>> n_action = 2
>>> batch = 5
>>>
>>> # Transforms a CatFrames in a stack of frames
>>> def reshape_cat(data: torch.Tensor):
...     return data.unflatten(-1, (time_steps, n_obs))
>>> # an actor that reads `time_steps` frames and outputs one action per frame
>>> # (actions are conditioned on the observation of `time_steps` in the past)
>>> actor_base = Seq(
...     Mod(reshape_cat, in_keys=["obs_cat"], out_keys=["obs_cat_reshape"]),
...     Mod(torch.nn.Linear(n_obs, n_action), in_keys=["obs_cat_reshape"], out_keys=["action"])
... )
>>> # Wrap the actor to dispatch the actions
>>> actor = MultiStepActorWrapper(actor_base, n_steps=time_steps)
>>>
>>> env = TransformedEnv(
...     SerialEnv(batch, lambda: GymEnv("CartPole-v1")),
...     Compose(
...         InitTracker(),
...         CatFrames(N=time_steps, in_keys=["observation"], out_keys=["obs_cat"], dim=-1)
...     )
... )
>>>
>>> print(env.rollout(100, policy=actor, break_when_any_done=False))
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([5, 100, 2]), device=cpu, dtype=torch.float32, is_shared=False),
        action_orig: Tensor(shape=torch.Size([5, 100, 6, 2]), device=cpu, dtype=torch.float32, is_shared=False),
        counter: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.int32, is_shared=False),
        done: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        is_init: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                is_init: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                obs_cat: Tensor(shape=torch.Size([5, 100, 24]), device=cpu, dtype=torch.float32, is_shared=False),
                observation: Tensor(shape=torch.Size([5, 100, 4]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                truncated: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([5, 100]),
            device=cpu,
            is_shared=False),
        obs_cat: Tensor(shape=torch.Size([5, 100, 24]), device=cpu, dtype=torch.float32, is_shared=False),
        observation: Tensor(shape=torch.Size([5, 100, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        terminated: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        truncated: Tensor(shape=torch.Size([5, 100, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([5, 100]),
    device=cpu,
    is_shared=False)

另请参阅

torchrl.envs.MultiStepEnvWrapper 是此包装器的 EnvBase 的对应对象：它包装一个环境并解绑动作，逐个元素执行。

forward(tensordict: TensorDictBase) → TensorDictBase

定义每次调用时执行的计算。

所有子类都应重写此方法。

注意

尽管前向传播的实现需要在此函数中定义，但您应该在之后调用 Module 实例而不是此函数，因为前者会处理注册的钩子，而后者则会静默忽略它们。

property init_key: NestedKey

批次中给定元素的初始步骤指示器。