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End-to-end training is best suited to settings where resources are genuinely scarce and feasibility matters, and leads the delay-adjusted policy value wherever capacity is scarce, once a waiting period carries even a small cost.
A strong, fixed, rule-based expert is built for Gin Rummy and used only as a yardstick, never for training, and the result is a lightweight, game-agnostic recipe that trains competitive agents without training on the expert, for any game a small model can handle, reported with robust statistics and released as a reusab...
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