Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty-Aware Policy Optimization: A Robust, Adaptive Trust Region
  Approach   In order for reinforcement learning techniques to be useful in real-world
decision making processes, they must be able to produce robust performance from
limited data. Deep policy optimization methods have achieved impressive results
on complex tasks, but their real-world adoption remains limited because they
often require significant amounts of data to succeed. When combined with small
sample sizes, these methods can result in unstable learning due to their
reliance on high-dimensional sample-based estimates. In this work, we develop
techniques to control the uncertainty introduced by these estimates. We
leverage these techniques to propose a deep policy optimization approach
designed to produce stable performance even when data is scarce. The resulting
algorithm, Uncertainty-Aware Trust Region Policy Optimization, generates robust
policy updates that adapt to the level of uncertainty present throughout the
learning process.

17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph