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.

中文翻译：

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多智能体捕食者回避的共识、合作学习和聚集

多智能体协调在各种任务中具有多种用途，是非常理想的。在自然界中，协同群集现象在与防御或逃离捕食者相关的应用中非常普遍。在本文中，提出了一种融合共识、协作学习和集群控制的混合多智能体系统，用于确定攻击捕食者的方向并学习以协调的方式远离它们。该系统是完全分布式的，仅需要相邻代理之间的通信。共识和协作强化学习的融合使代理能够在各种多代理协调任务中进行协作学习，但本文的重点是蜂拥而至，远离攻击掠食者。聚集的结果表明，代理能够有效地聚集到目标，而不会相互碰撞或障碍物。多种强化学习方法针对任务进行评估，协作学习利用函数逼近进行状态空间缩减，效果最佳。所提出的共识算法的结果表明，它提供了群体中代理之间快速准确的信息传输。进行模拟以在一个和两个捕食者环境中显示和验证所提出的混合系统，从而产生有效的合作学习行为。未来，使用共识来确定状态和强化学习来学习状态的系统可以应用于额外的多智能体任务。多种强化学习方法针对任务进行评估，协作学习利用函数逼近进行状态空间缩减，效果最佳。所提出的共识算法的结果表明，它提供了群体中代理之间快速准确的信息传输。进行模拟以在一个和两个捕食者环境中显示和验证所提出的混合系统，从而产生有效的合作学习行为。未来，使用共识来确定状态和强化学习来学习状态的系统可以应用于额外的多智能体任务。多种强化学习方法针对任务进行评估，协作学习利用函数逼近进行状态空间缩减，效果最佳。所提出的共识算法的结果表明，它提供了群体中代理之间快速准确的信息传输。进行模拟以在一个和两个捕食者环境中显示和验证所提出的混合系统，从而产生有效的合作学习行为。未来，使用共识来确定状态和强化学习来学习状态的系统可以应用于额外的多智能体任务。所提出的共识算法的结果表明，它提供了群体中代理之间快速准确的信息传输。进行模拟以在一个和两个捕食者环境中显示和验证所提出的混合系统，从而产生有效的合作学习行为。未来，使用共识来确定状态和强化学习来学习状态的系统可以应用于额外的多智能体任务。所提出的共识算法的结果表明，它提供了群体中代理之间快速准确的信息传输。进行模拟以在一个和两个捕食者环境中显示和验证所提出的混合系统，从而产生有效的合作学习行为。未来，使用共识来确定状态和强化学习来学习状态的系统可以应用于额外的多智能体任务。