Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robust Deep Reinforcement Learning for Security and Safety in Autonomous
  Vehicle Systems   To operate effectively in tomorrow's smart cities, autonomous vehicles (AVs)
must rely on intra-vehicle sensors such as camera and radar as well as
inter-vehicle communication. Such dependence on sensors and communication links
exposes AVs to cyber-physical (CP) attacks by adversaries that seek to take
control of the AVs by manipulating their data. Thus, to ensure safe and optimal
AV dynamics control, the data processing functions at AVs must be robust to
such CP attacks. To this end, in this paper, the state estimation process for
monitoring AV dynamics, in presence of CP attacks, is analyzed and a novel
adversarial deep reinforcement learning (RL) algorithm is proposed to maximize
the robustness of AV dynamics control to CP attacks. The attacker's action and
the AV's reaction to CP attacks are studied in a game-theoretic framework. In
the formulated game, the attacker seeks to inject faulty data to AV sensor
readings so as to manipulate the inter-vehicle optimal safe spacing and
potentially increase the risk of AV accidents or reduce the vehicle flow on the
roads. Meanwhile, the AV, acting as a defender, seeks to minimize the
deviations of spacing so as to ensure robustness to the attacker's actions.
Since the AV has no information about the attacker's action and due to the
infinite possibilities for data value manipulations, the outcome of the
players' past interactions are fed to long-short term memory (LSTM) blocks.
Each player's LSTM block learns the expected spacing deviation resulting from
its own action and feeds it to its RL algorithm. Then, the the attacker's RL
algorithm chooses the action which maximizes the spacing deviation, while the
AV's RL algorithm tries to find the optimal action that minimizes such
deviation.

32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex