Search

Mobile agent formation control is an important issue for the collective of robots . Especially when they move independently without human oversight, controlling the movement and forming a collective of agents is a critical and challenging task. In this work, we propose a method for applying the training algorithm to assist the formation of groups of agents in a leader scenario. To exercise control through supportive learning, we present the problem of formation as a Markov decision-making process. This allows us to use deep reinforcement learning to obtain the law of leadership of the successor leader. The feasibility and effectiveness of this control approach is tested in simulation. [ABSTRACT FROM AUTHOR]

This manuscript examines the path planning of an autonomous mobile robot with chaotic behaviour. The main question is what is the coverage of the environment in the autonomous generation of chaotic routes. The main problem with this type of chaotic trajectory is how to determine the control law, which allows to guarantee a good map coverage and at the same time to preserve the chaotic nature of the robot's movement. [ABSTRACT FROM AUTHOR]

This manuscript is focused on transfer learning methods for reinforcement learning agents. An preview of contemporary papers in area of transfer Leaning and Knowledge transfer. We provided the background and overview of knowledge transfer methods with an emphasis on the topics of reinforcement learning. [ABSTRACT FROM AUTHOR]

Autonomous agents in moving subspace should move in a particular formation. This article provides a brief overview of current Reinforcement Learning methods for formation control. The approaches to creating the formation and avoiding collisions between individual agents are considered. [ABSTRACT FROM AUTHOR]

In this article, we discuss deep learning methods for reinforcement learning agents. We study the application of multi-agent deep reinforcement learning techniques in agents. The main hypothesis is that deep reinforcement learning and collective behaviour approach demonstrate better performance than classic reinforcement learning. So autonomous agents are capable of discovering good solutions to the problem at hand by cooperate with other learners. [ABSTRACT FROM AUTHOR]