Multi-agent reinforcement learning (MARL) meth-ods face a curse of dimensionality in the
policy and value function representations as the number of agents increases. The …

Multi-agent reinforcement learning (MARL) has long been a significant research topic in
both machine learning and control systems. Recent development of (single-agent) deep …

A plethora of real world problems, such as the control of autonomous vehicles and drones,
packet delivery, and many others consists of a number of agents that need to take actions …

Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for
cooperative partially observable environments focus on finding factorized value functions …

Cooperative multi-agent reinforcement learning (MARL) has achieved significant results,
most notably by leveraging the representation-learning abilities of deep neural networks …

Multiagent reinforcement learning (MARL) algorithms have been demonstrated on complex
tasks that require the coordination of a team of multiple agents to complete. Existing works …

In multiagent reinforcement learning (MARL), it is crucial for each agent to model the relation
with its neighbors. Existing approaches usually resort to concatenate the features of multiple …

Multi-Agent Reinforcement Learning (MARL) is an increasingly important research field that
can model and control multiple large-scale autonomous systems. Despite its achievements …

Multi-agent reinforcement learning (MARL) has recently received considerable attention due
to its applicability to a wide range of real-world applications. However, achieving efficient …

In the domain of multi-agent reinforcement learning (MARL), the limited information
availability, complex agent interactions, and individual capabilities among agents often pose …