Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Only Relevant Information Matters: Filtering Out Noisy Samples to Boost
  RL   In reinforcement learning, policy gradient algorithms optimize the policy
directly and rely on sampling efficiently an environment. Nevertheless, while
most sampling procedures are based on direct policy sampling, self-performance
measures could be used to improve such sampling prior to each policy update.
Following this line of thought, we introduce SAUNA, a method where
non-informative transitions are rejected from the gradient update. The level of
information is estimated according to the fraction of variance explained by the
value function: a measure of the discrepancy between V and the empirical
returns. In this work, we use this metric to select samples that are useful to
learn from, and we demonstrate that this selection can significantly improve
the performance of policy gradient methods. In this paper: (a) We define
SAUNA's metric and introduce its method to filter transitions. (b) We conduct
experiments on a set of benchmark continuous control problems. SAUNA
significantly improves performance. (c) We investigate how SAUNA reliably
selects samples with the most positive impact on learning and study its
improvement on both performance and sample efficiency.

35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio