Multi-agent systems (MAS) are widely prevalent and crucially important in numerous real-
world applications, where multiple agents must make decisions to achieve their objectives in …

Due to the dynamic and unpredictable open-world setting, navigating complex environments
in Minecraft poses significant challenges for multi-agent systems. Agents must interact with …

With the power of large language models (LLMs), open-ended embodied agents can flexibly
understand human instructions, generate interpretable guidance strategies, and output …

The intermittent integration of renewable energy sources and the enhancement of energy-
saving awareness on the demand side have posed significant challenges to energy …

In multi-agent reinforcement learning (MARL), information fusion through relationship
modeling can effectively learn behavior strategies. However, the high dynamics among …

The difficulty of appropriately assigning credit is particularly heightened in cooperative
MARL with sparse reward, due to the concurrent time and structural scales involved …

Multi-agent deep reinforcement learning (MADRL) has shown remarkable advancements in
the past decade. However, most current MADRL models focus on task-specific short-horizon …

Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless
collaboration among agents, often represented by an underlying relation graph. Existing …

Real-world multi-agent tasks usually involve dynamic team composition with the emergence
of roles, which should also be a key to efficient cooperation in multi-agent reinforcement …

Obstacle avoidance in UAV swarms is crucial for ensuring the stability and safety of cluster
flights. However, traditional methods of swarm obstacle avoidance often fail to meet the …