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

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

Technological advancements of urban informatics and vehicular intelligence have enabled
connected smart vehicles as pervasive edge computing platforms for a plethora of powerful …

We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in
the centralized training with decentralized execution (CTDE) regime popularized recently …

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal
control (ATSC) in complex urban traffic networks, and deep neural networks further enhance …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

We study the problem of cooperative multi-agent reinforcement learning with a single joint
reward signal. This class of learning problems is difficult because of the often large …

Role-based learning holds the promise of achieving scalable multi-agent learning by
decomposing complex tasks using roles. However, it is largely unclear how to efficiently …

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

This work considers the problem of learning cooperative policies in complex, partially
observable domains without explicit communication. We extend three classes of single …