AI Solves Stratego With Imperfect Information on a Competitive Budget

Researchers have developed an AI system that defeated the best human Stratego player in recorded history, marking the first time AI has achieved top-level performance in this large imperfect-information board game. Unlike Chess or Go, Stratego involves hidden piece positions throughout the game, requiring deep probabilistic reasoning about opponent state — a fundamentally different challenge from perfect-information games. The system achieved this result at relatively modest computational cost compared to prior AI game-solving milestones, suggesting advances in search and belief-state estimation that may generalize beyond games. For AI researchers and developers working on decision-making under uncertainty, the techniques employed here — particularly around opponent modeling and hidden-state inference — have direct relevance to real-world agentic planning problems. This result extends the frontier of what reinforcement learning and game-theoretic AI can solve in the imperfect-information regime.