Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks   Deep learning classifiers are known to be inherently vulnerable to
manipulation by intentionally perturbed inputs, named adversarial examples. In
this work, we establish that reinforcement learning techniques based on Deep
Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and
verify the transferability of adversarial examples across different DQN models.
Furthermore, we present a novel class of attacks based on this vulnerability
that enable policy manipulation and induction in the learning process of DQNs.
We propose an attack mechanism that exploits the transferability of adversarial
examples to implement policy induction attacks on DQNs, and demonstrate its
efficacy and impact through experimental study of a game-learning scenario.

25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph