In distributed optimization of multi-agent systems, agents cooperate to minimize a global
function which is a sum of local objective functions. Motivated by applications including …

Networked microgrids (NMGs) provide a promising solution for accommodating various
distributed energy resources (DERs) and enhancing the system performance in terms of …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

Fixed-time and prescribed-time consensus control can bring an explicit estimate of the
settling time without dependence on initial conditions, which is important in providing control …

We explore value-based multi-agent reinforcement learning (MARL) in the popular
paradigm of centralized training with decentralized execution (CTDE). CTDE has an …

Most recently developed approaches to cooperative multi-agent reinforcement learning in
the\emph {centralized training with decentralized execution} setting involve estimating a …

In many real-world settings, a team of agents must coordinate its behaviour while acting in a
decentralised fashion. At the same time, it is often possible to train the agents in a …

Centralised training with decentralised execution is an important setting for cooperative
deep multi-agent reinforcement learning due to communication constraints during execution …

This article studies the distributed observer-based event-triggered bipartite tracking control
problem for stochastic nonlinear multiagent systems with input saturation. First, different from …

The state estimation problem of heterogeneous multi-agent systems with random transport
protocol is investigated in this paper. Due to the dependency of the agent dynamics and the …