Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Global optimality of softmax policy gradient with single hidden layer
  neural networks in the mean-field regime   We study the problem of policy optimization for infinite-horizon discounted
Markov Decision Processes with softmax policy and nonlinear function
approximation trained with policy gradient algorithms. We concentrate on the
training dynamics in the mean-field regime, modeling e.g., the behavior of wide
single hidden layer neural networks, when exploration is encouraged through
entropy regularization. The dynamics of these models is established as a
Wasserstein gradient flow of distributions in parameter space. We further prove
global optimality of the fixed points of this dynamics under mild conditions on
their initialization.

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The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex