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 …

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 …

Multi-agent systems can solve scientific issues related to complex systems that are difficult or
impossible for a single agent to solve through mutual collaboration and cooperation …

Synchronizing decisions across multiple agents in realistic settings is problematic since it
requires agents to wait for other agents to terminate and communicate about termination …

With the advancements of artificial intelligence (AI), we're seeing more scenarios that require
AI to work closely with other agents, whose goals and strategies might not be known …

Action advising is a knowledge transfer technique for reinforcement learning based on the
teacher-student paradigm. An expert teacher provides advice to a student during training in …

The state-of-the-art multi-agent reinforcement learning (MARL) methods provide promising
solutions to a variety of complex problems. Yet, these methods all assume that agents …

Traditional methods of pre-training, fine-tuning, and ensembling often overlook essential
relational data and task interconnections. To address this gap, our study presents a novel …

Transfer learning (TL) has shown great potential to improve Reinforcement Learning (RL)
efficiency by leveraging prior knowledge in new tasks. However, much of the existing TL …

Multiagent reinforcement learning (RL) training is usually difficult and time-consuming due to
mutual interference among agents. Safety concerns make an already difficult training …