Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence and Optimality of Policy Gradient Methods in Weakly Smooth
  Settings   Policy gradient methods have been frequently applied to problems in control
and reinforcement learning with great success, yet existing convergence
analysis still relies on non-intuitive, impractical and often opaque
conditions. In particular, existing rates are achieved in limited settings,
under strict regularity conditions. In this work, we establish explicit
convergence rates of policy gradient methods, extending the convergence regime
to weakly smooth policy classes with $L_2$ integrable gradient. We provide
intuitive examples to illustrate the insight behind these new conditions.
Notably, our analysis also shows that convergence rates are achievable for both
the standard policy gradient and the natural policy gradient algorithms under
these assumptions. Lastly we provide performance guarantees for the converged
policies.

29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys