论文标题
一种积极的学习方法，用于解决竞争性的多代理决策和控制问题
An active learning method for solving competitive multi-agent decision-making and control problems
论文作者
论文摘要
为了确定竞争者人群的固定动作概况，每个执行私人策略，我们介绍了一种新颖的主动学习方案，其中集中的外部观察者（或Entity）可以探测代理的反应并递归更新动作反应映射的简单局部参数估计。在非常普遍的工作假设（甚至没有假设存在固定轮廓）下，建立了足够的条件来评估所提出的主动学习方法的渐近性能，以便如果表征动作反应映射的参数会融合，则可以实现固定的动作概况。因此，这种条件也作为存在这种配置文件的证书。涉及典型的竞争性多代理控制和决策问题的广泛数值模拟说明了拟议的基于学习的方法的实际有效性。
To identify a stationary action profile for a population of competitive agents, each executing private strategies, we introduce a novel active-learning scheme where a centralized external observer (or entity) can probe the agents' reactions and recursively update simple local parametric estimates of the action-reaction mappings. Under very general working assumptions (not even assuming that a stationary profile exists), sufficient conditions are established to assess the asymptotic properties of the proposed active learning methodology so that, if the parameters characterizing the action-reaction mappings converge, a stationary action profile is achieved. Such conditions hence act also as certificates for the existence of such a profile. Extensive numerical simulations involving typical competitive multi-agent control and decision-making problems illustrate the practical effectiveness of the proposed learning-based approach.
