Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Distributed energy resources (DERs) are gaining prominence due to their advantages in
improving energy efficiency, reducing carbon emissions, and enhancing grid resilience …

Reinforcement Learning (RL) is a potent tool for sequential decision-making and has
achieved performance surpassing human capabilities across many challenging real-world …

Constrained cooperative multi-agent reinforcement learning (MARL) is an emerging
learning framework that has been widely applied to manage multi-agent systems, and many …

Multi-agent reinforcement learning (MARL) has achieved notable success in cooperative
tasks, demonstrating impressive performance and scalability. However, deploying MARL …

In this paper, we study safe data collection for the purpose of policy evaluation in tabular
Markov decision processes (MDPs). In policy evaluation, we are given a\textit {target} policy …

Multi-agent reinforcement learning (MARL) is employed to develop autonomous agents that
can learn to adopt cooperative or competitive strategies within complex environments …

Constrained cooperative multi-agent reinforcement learning (MARL) is an emerging
learning framework that has been widely applied to manage multi-agent systems, and many …

Reinforcement learning with general utilities has recently gained attention thanks to its
ability to unify several problems, including imitation learning, pure exploration, and safe RL …

We study the scalable multi-agent reinforcement learning (MARL) with general utilities,
defined as nonlinear functions of the team's long-term state-action occupancy measure. The …