A machine learning framework, equipped with a unitary Koopman structure, is designed to reconstruct Hamiltonian systems using either noise-perturbed or partially observational data. This framework can ...
Abstract: This article proposes and analyzes an accelerated reinforcement learning (RL) algorithm for discrete-time linear systems with unknown dynamics. The method achieves cubic convergence, ...
Abstract: The development of polarization-adjusted convolutional (PAC) codes has introduced a class of efficient designs for short packet transmission. In this contribution, aiming at more flexible ...
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