Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Improved Convergence Analysis of Stochastic Variance-Reduced Policy
  Gradient   We revisit the stochastic variance-reduced policy gradient (SVRPG) method
proposed by Papini et al. (2018) for reinforcement learning. We provide an
improved convergence analysis of SVRPG and show that it can find an
$\epsilon$-approximate stationary point of the performance function within
$O(1/\epsilon^{5/3})$ trajectories. This sample complexity improves upon the
best known result $O(1/\epsilon^2)$ by a factor of $O(1/\epsilon^{1/3})$. At
the core of our analysis is (i) a tighter upper bound for the variance of
importance sampling weights, where we prove that the variance can be controlled
by the parameter distance between different policies; and (ii) a fine-grained
analysis of the epoch length and batch size parameters such that we can
significantly reduce the number of trajectories required in each iteration of
SVRPG. We also empirically demonstrate the effectiveness of our theoretical
claims of batch sizes on reinforcement learning benchmark tasks.

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The categories of this paper are: hep-th
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The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys