<ul><li>The gap between local decisions and global consistency in current RL policies is unavoidable.</li><li>Classical vector calculus, through Green’s, Stokes’, and Gauss’ Theorems, reveals the symmetries and constraints that govern fields across space and time.</li><li>Green’s Theorem relates the circulation of a vector field to the divergence of the field, ensuring smooth and consistent flows.</li><li>Gauss' Theorem provides a global consistency constraint, ensuring that the total outward flow of decisions is accounted for by the behavior within the enclosed volume.</li></ul>

Bridging Dimensions in Reinforcement Learning with Green’s, Stokes’, and Gauss’ Theorems

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