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

Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

To promote better performance-bandwidth trade-off for multi-agent perception, we propose a
novel distilled collaboration graph (DiscoGraph) to model trainable, pose-aware, and …

Multi-agent collaborative perception as a potential application for vehicle-to-everything
communication could significantly improve the perception performance of autonomous …

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 …

Learning to cooperate is crucially important in multi-agent environments. The key is to
understand the mutual interplay between agents. However, multi-agent environments are …

Large Language Models (LLMs) have demonstrated impressive planning abilities in single-
agent embodied tasks across various domains. However, their capacity for planning and …

Multi-agent collaborative perception has recently received widespread attention as an
emerging application in driving scenarios. Despite the advancements in previous efforts …