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A Large Deviations Perspective on Policy Gradient Algorithms
Motivated by policy gradient methods in the context of reinforcement learning, we derive the first large deviation rate function for the iterates generated by stochastic gradient descent for possibly non-convex objectives satisfying a Polyak-Lojasiewicz condition.
Small errors in random zeroth-order optimization are imaginary
Most zeroth-order optimization algorithms mimic a first-order algorithm but replace the gradient of the objective function with some gradient estimator that can be computed from a small number of function evaluations.
Optimization and Control 65D25, 65G50, 65K05, 65Y04, 65Y20, 90C56
Topological Linear System Identification via Moderate Deviations Theory
Two dynamical systems are topologically equivalent when their phase-portraits can be morphed into each other by a homeomorphic coordinate transformation on the state space.
Optimization and Control
