  How do we formalize the challenge of credit assignment in reinforcement
learning? Common intuition would draw attention to reward sparsity as a key
contributor to difficult credit assignment and traditional heuristics would
look to temporal recency for the solution, calling upon the classic eligibility
trace. We posit that it is not the sparsity of the reward itself that causes
difficulty in credit assignment, but rather the \emph{information sparsity}. We
propose to use information theory to define this notion, which we then use to
characterize when credit assignment is an obstacle to efficient learning. With
this perspective, we outline several information-theoretic mechanisms for
measuring credit under a fixed behavior policy, highlighting the potential of
information theory as a key tool towards provably-efficient credit assignment.
