td1_advantage_estimate

class torchrl.objectives.value.functional.td1_advantage_estimate(gamma: float, state_value: torch.Tensor, next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, terminated: torch.Tensor | None = None, rolling_gamma: bool = None, time_dim: int = - 2)

TD(1) advantage estimate.

Parameters:

• gamma (scalar) – exponential mean discount.

• state_value (Tensor) – value function result with old_state input.

• 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 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.