Cooperative multi-agent reinforcement learning (c-MARL) is widely applied in safety-critical
scenarios, thus the analysis of robustness for c-MARL models is profoundly important …

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 …

Despite the fast development of multi-agent systems (MAS) and multi-agent reinforcement
learning (MARL) algorithms, there is a lack of unified evaluation platforms and commonly …

Despite the fast development of multi-agent reinforcement learning (MARL) methods, there
is a lack of commonly-acknowledged baseline implementation and evaluation platforms. As …

While deep neural networks (DNNs) have strengthened the performance of cooperative
multi-agent reinforcement learning (c-MARL), the agent policy can be easily perturbed by …

Training a multi-agent reinforcement learning (MARL) algorithm is more challenging than
training a single-agent reinforcement learning algorithm, because the result of a multi-agent …

Cooperative multi-agent reinforcement learning (CMARL) enables agents to achieve a
common objective. However, the safety (aka robustness) of the CMARL agents operating in …

Many complex multi-agent systems such as robot swarms control and autonomous vehicle
coordination can be modeled as Multi-Agent Reinforcement Learning (MARL) tasks. QMIX, a …

Centralized Training with Decentralized Execution (CTDE) has been a popular paradigm in
cooperative Multi-Agent Reinforcement Learning (MARL) settings and is widely used in …

Non-stationarity is one thorny issue in cooperative multi-agent reinforcement learning
(MARL). One of the reasons is the policy changes of agents during the learning process …