Role-based multi-agent reinforcement learning (MARL) holds the promise of achieving
scalable multi-agent cooperation by decomposing complex tasks through the concept of …

The role concept provides a useful tool to design and understand complex multi-agent
systems, which allows agents with a similar role to share similar behaviors. However …

Role-based learning is a promising approach to improving the performance of Multi-Agent
Reinforcement Learning (MARL). Nevertheless, without manual assistance, current role …

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 …

Recent developments in reinforcement learning have been able to derive optimal policies
for sophisticated and capable agents, and shown to achieve human-level performance on a …

Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in
recent years. For training efficiency and scalability, most of the MARL algorithms make all …

Multi-agent reinforcement learning holds the key for solving complex tasks that demand the
coordination of learning agents. However, strong coordination often leads to expensive …

Multi-Agent Reinforcement Learning (MARL) has made remarkable progress in recent
years. However, in most MARL methods, agents share a policy or value network, which is …

In collaborative multi-agent reinforcement learning (MARL), agents need to reach good
collaboration in an evolving environment. The introduction of the concept of role is an …

ABSTRACT Multi-Agent Deep Reinforcement Learning (MADRL) is a research field that
combines deep learning and multi-agent reinforcement learning. In complex tasks, a single …