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td0_advantage_estimate

class torchrl.objectives.value.functional.td0_advantage_estimate(gamma: float, state_value: Tensor, next_state_value: Tensor, reward: Tensor, done: Tensor, terminated: Optional[Tensor] = None)[source]

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.

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