Ivo Nowak

Researchers propose StructRL, a framework that recovers dynamic programming structure from the learning dynamics of distributional reinforcement learning without requiring an explicit model. By analyzing how return distributions evolve during training, the team identifies a temporal learning indicator that signals when states undergo their strongest updates, inducing an ordering consistent with structured information propagation. The work suggests that RL agents naturally exhibit dynamic programming-like behavior, offering a new lens on how learning unfolds as a structured process rather than uniform optimization.