This paper surveys the field of deep multiagent reinforcement learning (RL). The
combination of deep neural networks with RL has gained increased traction in recent years …

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 …

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 …

The exploitation of extra state information has been an active research area in multi-agent
reinforcement learning (MARL). QMIX represents the joint action-value using a non-negative …

Intrinsically motivated reinforcement learning aims to address the exploration challenge for
sparse-reward tasks. However, the study of exploration methods in transition-dependent …

Balancing the supply and demand for ride-sourcing companies is a challenging issue,
especially with real-time requests and stochastic traffic conditions of large-scale congested …

Cooperative game is a critical research area in the multi-agent reinforcement learning
(MARL). Global reward game is a subclass of cooperative games, where all agents aim to …

Sample efficiency and scalability to a large number of agents are two important goals for
multi-agent reinforcement learning systems. Recent works got us closer to those goals …

Many real-world problems require the coordination of multiple autonomous agents. Recent
work has shown the promise of Graph Neural Networks (GNNs) to learn explicit …

A variety of cooperative multi-agent control problems require agents to achieve individual
goals while contributing to collective success. This multi-goal multi-agent setting poses …