The advances in reinforcement learning have recorded sublime success in various domains.
Although the multi-agent domain has been overshadowed by its single-agent counterpart …

We explore value-based multi-agent reinforcement learning (MARL) in the popular
paradigm of centralized training with decentralized execution (CTDE). CTDE has an …

Role-based learning holds the promise of achieving scalable multi-agent learning by
decomposing complex tasks using roles. However, it is largely unclear how to efficiently …

Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve
complex cooperative tasks. Its success is partly because of parameter sharing among …

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 …

Communication is an effective mechanism for coordinating the behavior of multiple agents.
In the field of multi-agent reinforcement learning, agents can improve the overall learning …

Centralized Training for Decentralized Execution, where agents are trained offline using
centralized information but execute in a decentralized manner online, has gained popularity …

Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However,
there is a significant performance discrepancy between MAPG methods and state-of-the-art …

Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving
decentralised decision-making problems at scale. Research in the field has been growing …

Multi-agent reinforcement learning (MARL) has witnessed significant progress with the
development of value function factorization methods. It allows optimizing a joint action-value …