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

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 …

Abstract Recently, Multi-Robot Systems (MRS) have attained considerable recognition
because of their efficiency and applicability in different types of real-life applications. This …

Multi-agent path finding (MAPF) is an indispensable component of large-scale robot
deployments in numerous domains ranging from airport management to warehouse …

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 …

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 …

Abstract Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents
such that each agent reaches its goal and the agents do not collide. In recent years, variants …

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 …