An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning
This work addresses a foundational issue in reinforcement learning for AI researchers, offering a novel theoretical perspective that could improve learning efficiency.
The paper tackles the problem of credit assignment in reinforcement learning by proposing that information sparsity, not reward sparsity, is the key challenge, and uses information theory to define and measure credit, suggesting it as a tool for efficient learning.
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.