Reinforcement Learning with Multi-Step Transition Predictions: A Bellman Theory

Paper recorded by Signals 4 on 2026-10-23 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-23 on arXiv · recorded by Signals 4 on 2026-10-26

Category: cs.LG · 机器学习 · first seen 2026-10-26

Abstract

Multi-step predictions of future transition realizations can improve reinforcement learning, but their value remains difficult to quantify. Directly including these predictions in the state can lead to exponential state-space growth, making direct dynamic programming computationally expensive. We study discounted finite MDPs under a block-feedback protocol: at the start of each K-period block, the

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