Abstract Deep Reinforcement Learning has made significant progress in multi-agent
systems in recent years. The aim of this review article is to provide an overview of recent …

Deep Reinforcement Learning has made significant progress in multi-agent systems in
recent years. In this review article, we have focused on presenting recent approaches on …

The multi-agent pathfinding problem (MAPF) is the fundamental problem of planning paths
for multiple agents, where the key constraint is that the agents will be able to follow these …

Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world
robot deployments, from aerial swarms to warehouse automation. However, despite the …

We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF
algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed …

The multi-agent path-finding (MAPF) problem has recently received a lot of attention.
However, it does not capture important characteristics of many real-world domains, such as …

Multi-agent pathfinding (MAPF) is an area of expanding research interest. At the core of this
research area, numerous diverse search-based techniques were developed in the past 6 …

We study the offline Multi-Agent Pickup-and-Delivery (MAPD) problem, where a team of
agents has to execute a batch of tasks with release times in a known environment. To …

Abstract Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks
us to plan collision-free paths for a team of cooperative agents. In this work, we show that …

Recent years have shown a large increase in applications and research of problems that
include moving a fleet of physical robots. One particular application that is currently a multi …