Multi-agent systems (MASs) can autonomously learn to solve previously unknown tasks by means of each agent's individual intelligence as well as by collaborating and exploiting collective intelligence. This article considers a group of autonomous agents learning to track the same given reference trajectory in a possibly small number of trials. We propose a novel collective learning control method that combines iterative learning control (ILC) with a collective update strategy. We derive conditions for desirable convergence properties of such systems. We show that the proposed method allows the collective to combine the advantages of the agents' individual learning strategies and thereby overcomes trade-offs and limitations of single-agent ILC. This benefit is achieved by designing a heterogeneous collective, i.e., a different learning law is assigned to each agent. All theoretical results are confirmed in simulations and experiments with two-wheeled-inverted-pendulum robots (TWIPRs) that jointly learn to perform the desired maneuver.

翻译：多试剂系统(MAS)可以自主地学习,通过每个代理人的个别情报以及合作和利用集体情报,解决以前未知的任务。本条款认为一组自主代理人在可能为数不多的试验中学习追踪相同的参考轨迹。我们建议一种新型的集体学习控制方法,将迭代学习控制(ILC)与集体更新战略结合起来。我们为这些系统的适当趋同特性创造条件。我们表明,拟议方法允许集体将代理人个人学习战略的优势结合起来,从而克服单一代理人ILC的权衡和限制。通过设计一个不同的集体,即为每个代理人分配不同的学习法,从而实现了这一好处。所有理论结果都通过与两轮旋转式的旋转式机器人的模拟和实验得到证实,这些机器人共同学习进行所需的操作。