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arXiv · 2510.19372

On the Hardness of Reinforcement Learning with Transition Look-Ahead

Corentin Pla·Hugo Richard·Marc Abeille·Nadav Merlis·Vianney Perchet
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Abstract

We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of $\ell$ actions before deciding its course of action. While such predictive information can drastically improve the achievable performance, we show that using this information optimally comes at a potentially prohibitive computational cost. Specifically, we prove that optimal planning with one-step look-ahead ($\ell=1$) can be solved in polynomial time through a novel linear programming formulation. In contrast, for $\ell \geq 2$, the problem becomes NP-hard. Our results delineate a precise boundary between tractable and intractable cases for the problem of planning with transition look-ahead in reinforcement learning.

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Corentin Pla, Hugo Richard, Marc Abeille, Nadav Merlis, Vianney Perchet. 2025-10-22. On the Hardness of Reinforcement Learning with Transition Look-Ahead. https://arxiv.org/abs/2510.19372

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