Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linear Convergence of Entropy-Regularized Natural Policy Gradient with
  Linear Function Approximation   Natural policy gradient (NPG) methods with entropy regularization achieve
impressive empirical success in reinforcement learning problems with large
state-action spaces. However, their convergence properties and the impact of
entropy regularization remain elusive in the function approximation regime. In
this paper, we establish finite-time convergence analyses of
entropy-regularized NPG with linear function approximation under softmax
parameterization. In particular, we prove that entropy-regularized NPG with
averaging satisfies the \emph{persistence of excitation} condition, and
achieves a fast convergence rate of $\tilde{O}(1/T)$ up to a function
approximation error in regularized Markov decision processes. This convergence
result does not require any a priori assumptions on the policies. Furthermore,
under mild regularity conditions on the concentrability coefficient and basis
vectors, we prove that entropy-regularized NPG exhibits \emph{linear
convergence} up to a function approximation error.

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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: econ
12
The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol