The rapid advancement of chat-based language models has led to remarkable progress in
complex task-solving. However, their success heavily relies on human input to guide the …

Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …

The primary challenge in the development of large-scale artificial intelligence (AI) systems
lies in achieving scalable decision-making—extending the AI models while maintaining …

Communication is an effective mechanism for coordinating the behaviors of multiple agents,
broadening their views of the environment, and to support their collaborations. In the field of …

Both Minsky's" society of mind" and Schmidhuber's" learning to think" inspire diverse
societies of large multimodal neural networks (NNs) that solve problems by interviewing …

Abstract Purpose of Review Recent advances in sensing, actuation, and computation have
opened the door to multi-robot systems consisting of hundreds/thousands of robots, with …

Effective communication can improve coordination in cooperative multi-agent reinforcement
learning (MARL). One popular communication scheme is exchanging agents' local …

Utilizing messages from teammates can improve coordination in cooperative Multi-agent
Reinforcement Learning (MARL). To obtain meaningful information for decision-making …

Many real-world scenarios involve a team of agents that have to coordinate their policies to
achieve a shared goal. Previous studies mainly focus on decentralized control to maximize a …

In cooperative multi-agent reinforcement learning, centralized training and decentralized
execution (CTDE) has achieved remarkable success. Individual Global Max (IGM) …