reference/generated/torchrl.trainers.algorithms.configs.objectives.GAEConfig

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torchrl.trainers.algorithms.configs.objectives.GAEConfig

class torchrl.trainers.algorithms.configs.objectives.GAEConfig(_partial_: bool = False, gamma: float | None = None, lmbda: float | None = None, value_network: Any = None, average_gae: bool = False, differentiable: bool = False, vectorized: bool | None = None, skip_existing: bool | None = None, advantage_key: str | None = None, value_target_key: str | None = None, value_key: str | None = None, shifted: bool = False, device: Any = None, time_dim: int | None = None, auto_reset_env: bool = False, deactivate_vmap: bool = False, value_chunk_size: int | None = None, num_chunks: int | None = None, num_chunk: int | None = None, value_chunk_dim: int = 0, shifted_budget: int = 1, group_key: Any = None, valid_key: Any = None, _target_: str = 'torchrl.trainers.algorithms.configs.objectives._make_gae')

Hydra configuration for GAE.

Every kwarg accepted by GAE.__init__ is exposed as a field here. The valid_key field is applied through set_keys() by the factory.