Pursuit-evasion is the problem of capturing mobile targets with one or more pursuers. We
use deep reinforcement learning for pursuing an omnidirectional target with multiple …

It is of great challenge, though promising, to coordinate collective robots for hunting an
evader in a decentralized manner purely in light of local observations. In this paper, this …

We consider the problem of having a team of unmanned aerial vehicles (UAVs) and
unmanned ground vehicles (UGVs) pursue a second team of evaders while concurrently …

We propose a distributed algorithm for the cooperative pursuit of multiple evaders using
multiple pursuers in a bounded convex environment. The algorithm is suitable for …

A pursuit–evasion game is a classical maneuver confrontation problem in the multi-agent
systems (MASs) domain. An online decision technique based on deep reinforcement …

Learning strategic robot behavior--like that required in pursuit-evasion interactions--under
real-world constraints is extremely challenging. It requires exploiting the dynamics of the …

We consider the problem of having a team of unmanned ground vehicles (UGV) and
unmanned aerial vehicles (UAV) pursue a team of evaders while concurrently building a …

Recently, deep reinforcement learning (RL) methods have been applied successfully to
multi-agent scenarios. Typically, the observation vector for decentralized decision making is …

As one of the tiniest flying objects, unmanned aerial vehicles (UAVs) are often expanded as
the “swarm” to execute missions. In this article, we investigate the multiquadcopter and …

Developing a safe and efficient collision avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths without observing other …