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 …

A* is a search algorithm that has long been used in the pathfinding research community. Its
efficiency, simplicity, and modularity are often highlighted as its strengths compared to other …

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths with limited observation …

Effective communication is key to successful, decentralized, multi-robot path planning. Yet, it
is far from obvious what information is crucial to the task at hand, and how and when it must …

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 Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to their
goal locations without collisions. In this paper, we study the lifelong variant of MAPF, where …

Multi-agent Pickup and Delivery (MAPD) is a challenging industrial problem where a team of
robots is tasked with transporting a set of tasks, each from an initial location and each to a …

We describe a method for multirobot trajectory planning in known, obstacle-rich
environments. We demonstrate our approach on a quadrotor swarm navigating in a …

In this work we present Digital Life Project a framework utilizing language as the universal
medium to build autonomous 3D characters who are capable of engaging in social …

We study optimal multirobot path planning on graphs (MPP) over four minimization
objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance …