Young Zachary, Manh La Hung
Department of Computer Science and Engineering, Advanced Robotics and Automation (ARA) Laboratory, University of Nevada, Reno, NV, USA.
Int J Adv Robot Syst. 2020 Sep 1;17(5). doi: 10.1177/1729881420960342. Epub 2020 Sep 24.
Multiagent coordination is highly desirable with many uses in a variety of tasks. In nature, the phenomenon of coordinated flocking is highly common with applications related to defending or escaping from predators. In this article, a hybrid multiagent system that integrates consensus, cooperative learning, and flocking control to determine the direction of attacking predators and learns to flock away from them in a coordinated manner is proposed. This system is entirely distributed requiring only communication between neighboring agents. The fusion of consensus and collaborative reinforcement learning allows agents to cooperatively learn in a variety of multiagent coordination tasks, but this article focuses on flocking away from attacking predators. The results of the flocking show that the agents are able to effectively flock to a target without collision with each other or obstacles. Multiple reinforcement learning methods are evaluated for the task with cooperative learning utilizing function approximation for state-space reduction performing the best. The results of the proposed consensus algorithm show that it provides quick and accurate transmission of information between agents in the flock. Simulations are conducted to show and validate the proposed hybrid system in both one and two predator environments, resulting in an efficient cooperative learning behavior. In the future, the system of using consensus to determine the state and reinforcement learning to learn the states can be applied to additional multiagent tasks.
多智能体协调在各种任务中有许多用途，非常值得期待。在自然界中，协同集群现象在与防御或躲避捕食者相关的应用中非常常见。本文提出了一种混合多智能体系统，该系统整合了一致性、合作学习和集群控制，以确定攻击捕食者的方向，并学会以协调的方式远离它们。该系统完全分布式，仅需要相邻智能体之间进行通信。一致性和协作强化学习的融合使智能体能够在各种多智能体协调任务中进行合作学习，但本文重点关注远离攻击捕食者的集群行为。集群结果表明，智能体能够有效地聚集到目标位置，且彼此之间或与障碍物无碰撞。针对该任务评估了多种强化学习方法，其中利用函数逼近进行状态空间缩减的合作学习表现最佳。所提出的一致性算法结果表明，它能在集群中的智能体之间快速准确地传输信息。进行了仿真以展示和验证所提出的混合系统在单捕食者和双捕食者环境中的有效性，从而产生高效的合作学习行为。未来，使用一致性来确定状态和强化学习来学习状态的系统可应用于其他多智能体任务。
