AI

AI finally masters Stratego—on a budget

Kari Jaaskelainen

11 Nov 2025 — 1 min read

Big win for AI in hidden-information games. Stratego has long resisted top-tier AI—even with training runs costing millions. This new study cracks it.

Researchers achieved vastly superhuman Stratego play using self-play reinforcement learning plus a smart test-time search designed for imperfect information. The surprise: it worked with only a few thousand dollars of compute, not an industrial budget.

Why it matters: Stratego is a tough proxy for real-world decision-making under uncertainty.
How they did it: agents learn by playing themselves, then plan at move time while accounting for what’s hidden.
So what: cheaper, stronger methods could spread to many uncertain, adversarial settings.

Read the paper: http://arxiv.org/abs/2511.07312v1

Authors: Samuel Sokota, Eugene Vinitsky, Hengyuan Hu, J. Zico Kolter, Gabriele Farina

Paper: http://arxiv.org/abs/2511.07312v1

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AI ReinforcementLearning Stratego Games MachineLearning ImperfectInformation Research

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