Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning   Deep reinforcement learning (DRL) has made significant achievements in many
real-world applications. But these real-world applications typically can only
provide partial observations for making decisions due to occlusions and noisy
sensors. However, partial state observability can be used to hide malicious
behaviors for backdoors. In this paper, we explore the sequential nature of DRL
and propose a novel temporal-pattern backdoor attack to DRL, whose trigger is a
set of temporal constraints on a sequence of observations rather than a single
observation, and effect can be kept in a controllable duration rather than in
the instant. We validate our proposed backdoor attack to a typical job
scheduling task in cloud computing. Numerous experimental results show that our
backdoor can achieve excellent effectiveness, stealthiness, and sustainability.
Our backdoor's average clean data accuracy and attack success rate can reach
97.8% and 97.5%, respectively.

21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int