Information Theoretic Regret Bounds for Online Nonlinear Control Sham Kakade

Neural Information Processing Systems 

This work studies the problem of sequential control in an unknown, nonlinear dynamical system, where we model the underlying system dynamics as an unknown function in a known Reproducing Kernel Hilbert Space. This framework yields a general setting that permits discrete and continuous control inputs as well as non-smooth, non-differentiable dynamics.

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