td0_advantage_estimate

class torchrl.objectives.value.functional.td0_advantage_estimate(gamma: float, state_value: torch.Tensor, next_state_value: torch.Tensor, reward: torch.Tensor, done: torch.Tensor, terminated: torch.Tensor | None = None)

TD(0) advantage estimate of a trajectory.

Also known as bootstrapped Temporal Difference or one-step return.

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.

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