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vec_td_lambda_return_estimate

class torchrl.objectives.value.functional.vec_td_lambda_return_estimate(gamma, lmbda, next_state_value, reward, done, terminated: torch.Tensor | None = None, rolling_gamma: Optional[bool] = None, *, time_dim: int = - 2)[source]

Vectorized TD(\(\lambda\)) return estimate.

Parameters:
  • gamma (scalar, Tensor) – exponential mean discount. If tensor-valued, must be a [Batch x TimeSteps x 1] tensor.

  • lmbda (scalar) – trajectory discount.

  • next_state_value (Tensor) – value function result with new_state input. must be a [Batch x TimeSteps x 1] tensor

  • reward (Tensor) – reward of taking actions in the environment. must be a [Batch x TimeSteps x 1] or [Batch x TimeSteps] tensor

  • done (Tensor) – boolean flag for end of trajectory.

  • terminated (Tensor) – boolean flag for the end of episode. Defaults to done if not provided.

  • rolling_gamma (bool, optional) –

    if True, it is assumed that each gamma if a gamma tensor is tied to a single event:

    gamma = [g1, g2, g3, g4] value = [v1, v2, v3, v4] return = [

    v1 + g1 v2 + g1 g2 v3 + g1 g2 g3 v4, v2 + g2 v3 + g2 g3 v4, v3 + g3 v4, v4,

    ]

    if False, it is assumed that each gamma is tied to the upcoming trajectory:

    gamma = [g1, g2, g3, g4] value = [v1, v2, v3, v4] return = [

    v1 + g1 v2 + g1**2 v3 + g**3 v4, v2 + g2 v3 + g2**2 v4, v3 + g3 v4, v4,

    ]

    Default is True.

  • time_dim (int) – dimension where the time is unrolled. Defaults to -2.

All tensors (values, reward and done) must have shape [*Batch x TimeSteps x *F], with *F feature dimensions.

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