The role-oriented learning approach could improve the performance of multi-agent
reinforcement learning by decomposing complex multi-agent tasks into different roles …

In cooperative multi-agent reinforcement learning, role-based learning promises to reach
satisfactory policy learning through the decomposition of complicated tasks using roles …

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

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 …

In reinforcement learning, an agent interacts with an environment for learning its policy.
Designing the environment is an important part of training the agent because the change of …

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 …

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 …

Real-time strategy games are one of the important scenarios for studying multi-agent
reinforcement learning, and there have been some researchers who have achieved some …

Graph representation has gained widespread application across various machine learning
domains, attributed to its ability to discern correlations among input nodes. In the realm of …