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A one-step-per-layer smoother that approximates as Gaussian only each layer's affine output is derived that applies both to deterministic systems with noisy observations and to stochastic systems described by output statistics.
Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the most recent state alone. This paper studies system identification for discrete-time fractional-order linear time-invariant systems from a single observed trajectory of le...
A physics-informed RL (PIRL) framework that combines the complementary strengths of PINNs and RL for stochastic reach-avoid analysis, and develops a scheduled PIRL algorithm in which temporal-difference actor-critic learning first guides the critic toward a meaningful approximation of the reach-avoid value function.
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