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Sharp lower bounds are established for Gaussian approximation of Polyak-Ruppert averaged stochastic gradient descent with polynomially decreasing step sizes for Polyak-Ruppert averaged stochastic gradient descent with polynomially decreasing step sizes.
In this paper, we study the bias and high-order error bounds of the Linear Stochastic Approximation (LSA) algorithm with Polyak-Ruppert (PR) averaging under Markovian noise. We focus on the version of the algorithm with constant step size and propose a novel decomposition of the bias via a linearization technique. We a...
A statistical framework for Inverse Entropy-regularized Reinforcement Learning is developed that resolves ambiguity by combining entropy regularization with a least-squares reconstruction of the reward from the soft Bellman residual, leading to a unique and well-defined so-called least-squares reward consistent with th...
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