Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …

We consider the fully decentralized multi-agent reinforcement learning (MARL) problem,
where the agents are connected via a time-varying and possibly sparse communication …

In decentralized optimization, nodes cooperate to minimize an overall objective function that
is the sum (or average) of per-node private objective functions. Algorithms interleave local …

This paper considers the problem of distributed optimization over time-varying graphs. For
the case of undirected graphs, we introduce a distributed algorithm, referred to as DIGing …

We consider distributed optimization by a collection of nodes, each having access to its own
convex function, whose collective goal is to minimize the sum of the functions. The …

This paper develops a distributed cooperative control logic to determine conflict-free
trajectories for connected and automated vehicles (CAVs) in signal-free intersections. The …

This paper reviews some main results and progress in distributed multi-agent coordination,
focusing on papers published in major control systems and robotics journals since 2006 …

We consider distributed optimization problems in which a number of agents are to seek the
optimum of a global objective function through merely local information sharing. The …

This work addresses decentralized online optimization in nonstationary environments. A
network of agents aim to track the minimizer of a global, time-varying, and convex function …