AWhat changed
Combined regularized payoff-learning dynamics with model-free deep self-play, fine-tuning and test-time policy filtering to reach expert-level Stratego Classic performance without game-tree search or human demonstrations.
Scientific scope
Combined regularized payoff-learning dynamics with model-free deep self-play, fine-tuning and test-time policy filtering to reach expert-level Stratego Classic performance without game-tree search or human demonstrations. Idealized regularized-dynamics convergence and a practical deep agent's finite online ranking are different evidence. Function approximation, fine-tuning and post-processing do not constitute a WRS-certified full-game equilibrium or strong solution. Ranking concerns the reported2022Gravon sample, not an enduring all-platform world rank. Source convergence arguments inherit earlier regularization work.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Idealized regularized-dynamics convergence and a practical deep agent's finite online ranking are different evidence. Function approximation, fine-tuning and post-processing do not constitute a WRS-certified full-game equilibrium or strong solution. Ranking concerns the reported2022Gravon sample, not an enduring all-platform world rank. Source convergence arguments inherit earlier regularization work.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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