vec_generalized_advantage_estimate

class torchrl.objectives.value.functional.vec_generalized_advantage_estimate(gamma: Union[float, torch.Tensor], lmbda: Union[float, torch.Tensor], state_value: torch.Tensor, next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, terminated: torch.Tensor | None = None, *, time_dim: int = - 2)

Vectorized Generalized advantage estimate of a trajectory.

Refer to “HIGH-DIMENSIONAL CONTINUOUS CONTROL USING GENERALIZED ADVANTAGE ESTIMATION” https://arxiv.org/pdf/1506.02438.pdf for more context.

Parameters:

• gamma (scalar) – exponential mean discount.

• lmbda (scalar) – trajectory 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.

• 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.