Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Proximal/Trust Region Policy Optimization Attains Globally
  Optimal Policy   Proximal policy optimization and trust region policy optimization (PPO and
TRPO) with actor and critic parametrized by neural networks achieve significant
empirical success in deep reinforcement learning. However, due to nonconvexity,
the global convergence of PPO and TRPO remains less understood, which separates
theory from practice. In this paper, we prove that a variant of PPO and TRPO
equipped with overparametrized neural networks converges to the globally
optimal policy at a sublinear rate. The key to our analysis is the global
convergence of infinite-dimensional mirror descent under a notion of one-point
monotonicity, where the gradient and iterate are instantiated by neural
networks. In particular, the desirable representation power and optimization
geometry induced by the overparametrization of such neural networks allow them
to accurately approximate the infinite-dimensional gradient and iterate.

25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph