This is the pre-proceedings for the RLC 2026. You may expect minor changes.
Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
Will be presented at the Reinforcement Learning Conference (RLC), Montréal, Quebec, Canada, August 15–17, 2026.
Keywords: Strategic Decision-Making, Long-Term Planning, Reinforcement Learning,
Long-term planning, as in reinforcement learning (RL), is often hard to make interpretable to humans as it involves strategies: collections of actions that work toward a goal with potentially complex dependencies. In particular, some actions are taken at the expense of short-term benefit to enable future actions with even greater returns. In this paper, we quantify such dependencies between planned actions with strategic link scores: the drop in the likelihood of an earlier action under the constraint that a follow-up action is no longer available. We use strategic link scores to (i) explain black-box RL agents by identifying strategically-linked pairs among decisions they make, and (ii) improve the worst-case performance of decision support systems by distinguishing whether recommended actions can be adopted as standalone improvements, or whether they are strategically linked hence require a commitment to a broader strategy to be effective. We demonstrate these use cases with maze-solving and chess-playing examples as well as simulated healthcare, traffic, and crisis negotiation environments.
Alihan Hüyük, Jonas B Raedler, Leo Benac, and Finale Doshi-Velez. "Strategically-Linked Decisions in Long-Term Planning and Reinforcement Learning." Reinforcement Learning Journal, vol. 7, 2026, pp. TBD.
BibTeX:@article{hyk2026strategicallylinked,
title={Strategically-Linked Decisions in Long-Term Planning and Reinforcement Learning},
author={Alihan Hüyük and Jonas B Raedler and Leo Benac and Finale Doshi-Velez},
journal={Reinforcement Learning Journal},
volume={7},
pages={},
year={2026}
}