tf_agents.trajectories.transition

Returns a TimeStep with step_type set equal to StepType.MID.

tf_agents.trajectories.transition(
    observation: tf_agents.typing.types.NestedTensorOrArray,
    reward: tf_agents.typing.types.NestedTensorOrArray,
    discount: tf_agents.typing.types.Float = 1.0,
    outer_dims: Optional[types.Shape] = None
) -> tf_agents.trajectories.TimeStep

Used in the notebooks

Used in the tutorials

For TF transitions, the batch size is inferred from the shape of reward.

If discount is a scalar, and observation contains Tensors, then discount will be broadcasted to match reward.shape.

observation A NumPy array, tensor, or a nested dict, list or tuple of arrays or tensors.
reward A NumPy array, tensor, or a nested dict, list or tuple of arrays or tensors.
discount (optional) A scalar, or 1D NumPy array, or tensor.
outer_dims (optional) If provided, it will be used to determine the batch dimensions. If not, the batch dimensions will be inferred by reward's shape. If reward is a vector, but not batched use ().

A TimeStep.

ValueError If observations are tensors but reward's statically known rank is not 0 or 1.