class torchrl.objectives.value.functional.td1_advantage_estimate(gamma: float, state_value: Tensor, next_state_value: Tensor, reward: Tensor, done: Tensor, rolling_gamma: Optional[bool] = None, time_dim: int = - 2)[source]

TD(1) advantage estimate.

  • 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 episode.

  • 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.


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources