Two distinguishable problems share the name:

Structural credit assignment asks which components of a system deserve credit for an outcome. Backpropagation is a complete answer to this within a differentiable model.

Temporal credit assignment asks which of the actions taken at times \(t_1 < t_2 < \dots < t_n\) produced a reward observed at time \(T\). This has no comparably complete answer. Discounted returns bound the problem by making distant consequences cheap to ignore:

\[ G_t = \sum_{k=0}^{\infty} \gamma^{k} r_{t+k+1}, \qquad 0 < \gamma < 1 \]

(1)

which is a mathematical convenience with a substantive cost — it makes long-horizon consequences invisible by construction. An agent whose decisions pay off in weeks cannot be trained by an objective that discounts them to zero in hours. See OP-001.