IEEE Computational Intelligence Magazine - August 2022 - 13

exploration rate, finally the exploration
rate is transmitted to thalamus to calculate
the corresponding action probability.
This action selection mechanism is
introduced to the actor-critic model of
the basal ganglia, combining with the
cerebellum model based on the developmental
network to construct a new
hybrid neuromodulatory model to select
the action of the agent. Both the simulation
comparison with other four traditional
action selection policies and the
physical experiment results demonstrate
the potential of the proposed neuromodulatory
model in action selection. "
IEEE Transactions on Emerging
Topics in Computational
Intelligence
A Survey of Embodied AI: From Simulators
to Research Tracks, by J. Duan, S.
Yu, H. L. Tan, H. Zhu, and C. Tan,
IEEE Transactions on Emerging Topics in
Computational Intelligence, Vol. 6,
No. 2, Apr 2022, pp. 230-244.
Digital Object
Identifier: 10.1109/
TETCI.2022.3141105
" There has been an emerging paradigm
shift from the era of " internet AI " to
" embodied AI, " where AI algorithms and
agents no longer learn from datasets of
images, videos or text curated primarily
from the internet. Instead, they learn
through interactions with their environments
from an egocentric perception
similar to humans. Consequently, there
has been substantial growth in the
demand for embodied AI simulators to
support various embodied AI research
tasks. This growing interest in embodied
AI is beneficial to the greater pursuit of
Artificial General Intelligence (AGI), but
there has not been a contemporary and
comprehensive survey of this field. This
paper aims to provide an encyclopedic
survey for the field of embodied AI, from
its simulators to its research. By evaluating
nine current embodied AI simulators
with our proposed seven features, this
paper aims to understand the simulators
in their provision for use in embodied AI
research and their limitations. Lastly, this
paper surveys the three main research
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tasks in embodied AI-visual exploration,
visual navigation and embodied question
answering (QA), covering the state-ofthe-art
approaches, evaluation metrics and
datasets. Finally, with the new insights
revealed through surveying the field, the
paper will provide suggestions for simulator-for-task
selections and recommendations
for the future directions of the field. "
IEEE Transactions on
Artificial Intelligence
Challenges and Countermeasures for
Adversarial Attacks on Deep Reinforcement
Learning, by I. Ilahi, M. Usama, J.
Qadir, M. U. Janjua, A. Al-Fuqaha, D.
T. Hoang, and D. Niyato, IEEE Transactions
on Artificial Intelligence, Vol. 3,
No. 2, Apr 2022, pp 90-109.
Digital Object Identifier: 10.1109/
TAI.2021.3111139
" Deep reinforcement learning (DRL)
has numerous applications in the real
world, thanks to its ability to achieve
high performance in a range of environments
with little manual oversight.
Despite its great advantages, DRL is susceptible
to adversarial attacks, which precludes
its use in real-life critical systems
and applications (e.g., smart grids, traffic
controls, and autonomous vehicles)
unless its vulnerabilities are addressed and
mitigated. To address this problem, we
provide a comprehensive survey that discusses
emerging attacks on DRL-based
systems and the potential countermeasures
to defend against these attacks. We
first review the fundamental background
on DRL and present emerging adversarial
attacks on machine learning techniques.
We then investigate the
vulnerabilities that an adversary can
exploit to attack DRL along with stateof-the-art
countermeasures to prevent
such attacks. Finally, we highlight open
issues and research challenges for developing
solutions to deal with attacks on
DRL-based intelligent systems. "
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AUGUST 2022 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 13
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IEEE Computational Intelligence Magazine - August 2022

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