StructRL: Recovering Dynamic Programming Structure from Learning Dynamics in Distributional Reinforcement Learning
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 w...
| Authors | Ivo Nowak |
|---|---|
| Primary category | cs.LG |
| Published | 9 April 2026 |
| Last updated at source | 19 April 2026 |
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