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

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

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

Many tasks in AI require the collaboration of multiple agents. Typically, the communication
protocol between agents is manually specified and not altered during training. In this paper …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

Potential games are arguably one of the most important and widely studied classes of
normal form games. They define the archetypal setting of multi-agent coordination as all …

In this paper, a multiagent-based model is used to study distributed energy management in
a microgrid (MG). The suppliers and consumers of electricity are modeled as autonomous …

In next-generation wireless networks, high-mobility unmanned aerial vehicles (UAVs) are
promising to provide content coverage, where users can receive sufficient requested content …

We examine global non-asymptotic convergence properties of policy gradient methods for
multi-agent reinforcement learning (RL) problems in Markov potential games (MPGs). To …