צפייה מועדפת מאפשרת גילוי מדויק של תכונות פוליטי בעולם הפיזי

תקציר מקורי באנגליתarXiv:2512.08463v2 Announce Type: replace Abstract: We study how privileged information about a physical system affects the discovery of high-performing policies when training a reinforcement learning agent directly in the physical world. We let the agent control a cylinder in a tabletop water channel to maximize or minimize drag. The flow is chaotic and difficult to model or simulate accurately and good strategies are not obvious beforehand. Decades-old experimental studies provide recipes for simple, high-performance, periodic open-loop policies that increase drag by 26.6% +/- 0.7% and reduce it by 29.7% +/- 1.3%. With dense observations of the cylinder wake, the agent learns within tens of minutes policies that increase drag by 25.5% +/- 0.9% and reduce it by 32.4% +/- 1.6%. We record a