CEMPlanner

class torchrl.modules.CEMPlanner(*args, **kwargs)

CEMPlanner 模块。

参考：用于优化的交叉熵方法，Botev 等人 2013

当给定包含初始状态的 TensorDict 时，此模块将执行 CEM 计划步骤。CEM 计划步骤通过从均值为零、方差为一的正态分布中采样动作来执行。然后，使用采样的动作在环境中执行展开。然后对使用展开获得的累积奖励进行排名。我们选择前 k 个情节，并使用它们的动作来更新动作分布的均值和标准差。CEM 计划步骤会重复执行指定次数。

对模块的调用会返回在给定计划范围的情况下根据经验最大化回报的动作

参数：

• env (EnvBase) – 用于执行计划步骤的环境（可以是 ModelBasedEnv 或 EnvBase）。

• planning_horizon (int) – 模拟轨迹的长度

• optim_steps (int) – MPC 规划器使用的优化步骤数

• num_candidates (int) – 从高斯分布中采样的候选者数量。

• top_k (int) – 用于更新高斯分布的均值和标准差的前 k 个候选者数量。

• reward_key (str, optional) – 用于检索奖励的 TensorDict 中的键。默认为“reward”。

• action_key (str, optional) – 用于存储动作的 TensorDict 中的键。默认为“action”

示例

>>> from tensordict import TensorDict
>>> from torchrl.data import CompositeSpec, UnboundedContinuousTensorSpec
>>> from torchrl.envs.model_based import ModelBasedEnvBase
>>> from torchrl.modules import SafeModule
>>> class MyMBEnv(ModelBasedEnvBase):
...     def __init__(self, world_model, device="cpu", dtype=None, batch_size=None):
...         super().__init__(world_model, device=device, dtype=dtype, batch_size=batch_size)
...         self.state_spec = CompositeSpec(
...             hidden_observation=UnboundedContinuousTensorSpec((4,))
...         )
...         self.observation_spec = CompositeSpec(
...             hidden_observation=UnboundedContinuousTensorSpec((4,))
...         )
...         self.action_spec = UnboundedContinuousTensorSpec((1,))
...         self.reward_spec = UnboundedContinuousTensorSpec((1,))
...
...     def _reset(self, tensordict: TensorDict) -> TensorDict:
...         tensordict = TensorDict(
...             {},
...             batch_size=self.batch_size,
...             device=self.device,
...         )
...         tensordict = tensordict.update(
...             self.full_state_spec.rand())
...         tensordict = tensordict.update(
...             self.full_action_spec.rand())
...         tensordict = tensordict.update(
...             self.full_observation_spec.rand())
...         return tensordict
...
>>> from torchrl.modules import MLP, WorldModelWrapper
>>> import torch.nn as nn
>>> world_model = WorldModelWrapper(
...     SafeModule(
...         MLP(out_features=4, activation_class=nn.ReLU, activate_last_layer=True, depth=0),
...         in_keys=["hidden_observation", "action"],
...         out_keys=["hidden_observation"],
...     ),
...     SafeModule(
...         nn.Linear(4, 1),
...         in_keys=["hidden_observation"],
...         out_keys=["reward"],
...     ),
... )
>>> env = MyMBEnv(world_model)
>>> # Build a planner and use it as actor
>>> planner = CEMPlanner(env, 10, 11, 7, 3)
>>> env.rollout(5, planner)
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False),
        done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False),
        hidden_observation: Tensor(shape=torch.Size([5, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        next: TensorDict(
            fields={
                done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False),
                hidden_observation: Tensor(shape=torch.Size([5, 4]), device=cpu, dtype=torch.float32, is_shared=False),
                reward: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False),
                terminated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
            batch_size=torch.Size([5]),
            device=cpu,
            is_shared=False),
        terminated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
    batch_size=torch.Size([5]),
    device=cpu,
    is_shared=False)

planning(tensordict: TensorDictBase) → Tensor

执行 MPC 计划步骤。

参数：

td (TensorDict) – 用于执行计划步骤的 TensorDict。