Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …

Multiagent Reinforcement Learning (MARL) plays a pivotal role in intelligent vehicle
systems, offering solutions for complex decision-making, coordination, and adaptive …

Despite the broad application of Machine Learning models as a Service (MLaaS), they are
vulnerable to model stealing attacks. These attacks can replicate the model functionality by …

Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such
as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are …

This research explores the vulnerability of selective reincarnation, a concept in Multi-Agent
Reinforcement Learning (MARL), in response to observation poisoning attacks. Observation …

Recent studies have shown that cooperative multi-agent deep reinforcement learning (c-
MADRL) is under the threat of backdoor attacks. Once a backdoor trigger is observed, it will …

Multiagent deep reinforcement learning (MADRL) has been recently applied in many fields,
including industry 5.0, but it is sensitive to adversarial attacks. Although adversarial attacks …

Cooperative multiagent reinforcement learning approaches are increasingly being used to
make decisions in contested and dynamic environments, which tend to be wildly different …

In recent years, deep reinforcement learning (DRL) has found extensive applications in the
field of traffic signal control (TSC). However, many studies have demonstrated the …

Reinforcement learning agents are susceptible to evasion attacks during deployment. In
single-agent environments, these attacks can occur through imperceptible perturbations …