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By recasting the Hamilton-Jacobi-Bellman equation as a tractable Monte Carlo estimation, this work stabilizes physics-informed RL and unlocks its potential for high-dimensional control tasks.
Ditch slow, verbose language-based action planning: this diffusion model directly predicts discrete, kinematically feasible waypoints for faster, more precise autonomous driving.
