Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

This paper serves to introduce the reader to the field of multi-agent reinforcement learning
(MARL) and its intersection with methods from the study of causality. We highlight key …

We study reinforcement learning (RL) in a setting with a network of agents whose states and
actions interact in a local manner where the objective is to find localized policies such that …

We study a multi-agent reinforcement learning (MARL) problem where the agents interact
over a given network. The goal of the agents is to cooperatively maximize the average of …

Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed
with the assumption that agents' policies are based on accurate state information. However …

This paper studies the trade-off between the degree of decentralization and the performance
of a distributed controller in a linear-quadratic control setting. We study a system of …

Currently, most edge-based Deep Reinforcement Learning (Deep-RL) applications have
been deployed in the edge network, however, their mainstream studies are still short of …

We study multi-agent reinforcement learning (MARL) in a stochastic network of agents. The
objective is to find localized policies that maximize the (discounted) global reward. In …

We study the optimal control of multiple-input and multiple-output dynamical systems via the
design of neural network-based controllers with stability and output tracking guarantees …

The analysis and control of large-population systems is of great interest to diverse areas of
research and engineering, ranging from epidemiology over robotic swarms to economics …