td_lambda_return_estimate

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

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

Parameters:

• gamma (scalar) – exponential mean discount.

• lmbda (scalar) – trajectory discount.

• next_state_value (Tensor) – value function result with new_state input.

• reward (Tensor) – reward of taking actions in the environment.

• 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 of 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.