Abstract Purpose of Review Robotic patrolling aims at protecting a physical environment by
deploying a team of one or more autonomous mobile robots in it. A key problem in this …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a
variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

A team of autonomous decision-making robots can be employed for some critical tasks, such
as disaster detection, plant protection, and military reconnaissance. The use of such team of …

This paper considers the problem of planning trajectories for both single and multiple
sensing robots to best estimate a spatiotemporal field in a dynamic environment. The robots …

Most cleaning robots have a good cleaning performance for small environments such as
houses but require a longer cleaning time due to problems such as slow cleaning progress …

Multi-robot teams are useful in a variety of task allocation domains such as warehouse
automation and surveillance. Robots in such domains perform tasks at given locations and …

Deep reinforcement learning has achieved great successes in recent years, however, one
main challenge is the sample inefficiency. In this paper, we focus on how to use action …