Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Abstract Purpose of Review This review summarizes the broad roles that communication
formats and technologies have played in enabling multi-robot systems. We approach this …

We consider the problem of designing distributed collision-avoidance multi-agent control in
large-scale environments with potentially moving obstacles, where a large number of agents …

Multi-agent collaborative navigation is prevalent in modern transportation systems, including
delivery logistics, warehouse automation, and personalised tourism, where multiple moving …

Learning distributed cooperative policies for large-scale multirobot systems remains a
challenging task in the multiagent reinforcement learning (MARL) context. In this work, we …

Distributed, scalable, and safe control of large-scale multi-agent systems is a challenging
problem. In this paper, we design a distributed framework for safe multi-agent control in …

While many multi-robot coordination problems can be solved optimally by exact algorithms,
solutions are often not scalable in the number of robots. Multi-Agent Reinforcement Learning …

The problem of assigning a set of spatial goals to a team of robots plays a crucial role in
various multi-robot planning applications including, but not limited to exploration, search and …

Deep reinforcement learning (RL) is an advancing learning tool to handle robotics control
problems. However, it typically suffers from sample efficiency and effectiveness. The …

The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the
safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully …