CEMPlanner

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

CEMPlanner 模块。

参考：The cross-entropy method for optimization, Botev et al. 2013 (交叉熵优化方法，Botev 等人，2013)

当给定包含初始状态的 TensorDict 时，此模块将执行 CEM 规划步骤。 CEM 规划步骤通过从均值为零、单位方差的高斯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 rollout。然后对 rollout 获得的累积奖励进行排名。我们选择前 k 个 episode，并使用它们的动作来更新动作分布的均值和标准差。 CEM 规划步骤重复指定的步骤数。

调用该模块会返回根据规划范围经验性地最大化回报的动作

参数:

• env (EnvBase) – 要在其上执行规划步骤的环境（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数量。

• top_k (int) – 用于更新高斯分布的均值和标准差的顶部候选数量。

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

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

示例

>>> from tensordict import TensorDict
>>> from torchrl.data import Composite, Unbounded
>>> 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 = Composite(
...             hidden_observation=Unbounded((4,))
...         )
...         self.observation_spec = Composite(
...             hidden_observation=Unbounded((4,))
...         )
...         self.action_spec = Unbounded((1,))
...         self.reward_spec = Unbounded((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 规划步骤。

参数:

td (TensorDict) – 要在其上执行规划步骤的 TensorDict。