StructRL: Recovering Dynamic Programming Structure from Learning Dynamics in Distributional Reinforcement Learning

arXiv cs.AI / 4/13/2026

💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper argues that, unlike standard reward- and TD-error-driven RL updates, dynamic programming exploits structured information propagation across the state space.
It shows that such global structure can be inferred from distributional RL learning dynamics by examining how return distributions evolve over time.
The authors introduce a temporal learning indicator t*(s) that marks when each state receives its strongest learning update during training, enabling an ordering over states resembling dynamic-programming propagation.
Based on this ordering, they propose StructRL, which uses these signals to guide sampling so training follows the emergent propagation structure.
Preliminary empirical results suggest that distributional learning dynamics can recover and leverage dynamic programming-like structure without an explicit environment model, reframing RL as structured propagation rather than uniform optimization.

Abstract

Reinforcement learning is typically treated as a uniform, data-driven optimization process, where updates are guided by rewards and temporal-difference errors without explicitly exploiting global structure. In contrast, dynamic programming methods rely on structured information propagation, enabling efficient and stable learning. In this paper, we provide evidence that such structure can be recovered from the learning dynamics of distributional reinforcement learning. By analyzing the temporal evolution of return distributions, we identify signals that capture when and where learning occurs in the state space. In particular, we introduce a temporal learning indicator t*(s) that reflects when a state undergoes its strongest learning update during training. Empirically, this signal induces an ordering over states that is consistent with a dynamic programming-style propagation of information. Building on this observation, we propose StructRL, a framework that exploits these signals to guide sampling in alignment with the emerging propagation structure. Our preliminary results suggest that distributional learning dynamics provide a mechanism to recover and exploit dynamic programming-like structure without requiring an explicit model. This offers a new perspective on reinforcement learning, where learning can be interpreted as a structured propagation process rather than a purely uniform optimization procedure.