Multi-agent formation as well as obstacle avoid-ance is one of the most actively studied
topics in the field of multi-agent systems. Although some classic controllers like model …

In this paper, an incremental centralized formation system is developed for controlling the
multirobot formation with joining robots, and a nonlinear model predictive control (NMPC) …

This paper introduces a reinforcement learning technique with an internal reward for a multi-
agent cooperation task. The proposed methods is an extension of Q-learning which changes …

This article presents a macroscopic swarm foraging behavior obtained using deep
reinforcement learning. The selected behavior is a complex task in which a group of simple …

To solve the problems of difficult control law design, poor portability, and poor stability of
traditional multi-agent formation obstacle avoidance algorithms, a multi-agent formation …

Cooperative nature in multiagent system inculcates more understanding and data by
sharing the resources. So cooperation in a multiagent system gives higher efficiency and …

针对经典强化学习的多智能体编队存在通信和计算资源消耗大的问题, 本文引入事件驱动控制
机制, 智能体的动作决策无须按固定周期进行, 而依赖于事件驱动条件更新智能体动作 …

The paper gives the novel framework for dynamic decision making in the retail shop
application based on proposed improved Nash Q-learning by Fault Pair Algorithm …

Isolating (singling) an individual from a group can be essential for protection, rescue or
capture tasks. In this paper a system with multiple shepherds who must coordinate the …

To address issues such as the need for controller design based on model information in
UAV formation and the low level of UVA intelligence, deep reinforcement learning is used to …