标题： 具有鲁棒同步和策略适应性的网络异构智能体 显示英文标题

标题： Robust synchronization and policy adaptation for networked heterogeneous agents

摘要： 我们提出了一种针对具有系统不确定性及输入幅度饱和的非线性异构代理的领导者-跟随者网络的鲁棒自适应在线同步方法。 使用分布式输入幅度饱和自适应控制与强化学习（DMSAC-RL）实现同步，该方法通过使用强化学习（RL）策略改进了在现成模型上训练的策略的实证性能。 领导者观察参考模型的性能，而跟随者观察它们所连接的代理的状态和动作，但不观察参考模型。 领导者和跟随者可能与RL控制策略被训练的参考模型不同。 DMSAC-RL 使用一个内部循环，以增强输入的形式调整代理的学习策略，以解决分布式控制问题，包括输入匹配的不确定性参数。 我们证明了异构网络的同步误差是统一最终有界的（UUB）。 多输入多输出（MIMO）系统的网络数值分析支持我们的理论结果。 显示英文摘要

摘要： We propose a robust adaptive online synchronization method for leader-follower networks of nonlinear heterogeneous agents with system uncertainties and input magnitude saturation. Synchronization is achieved using a Distributed input Magnitude Saturation Adaptive Control with Reinforcement Learning (DMSAC-RL), which improves the empirical performance of policies trained on off-the-shelf models using Reinforcement Learning (RL) strategies. The leader observes the performance of a reference model, and followers observe the states and actions of the agents they are connected to, but not the reference model. The leader and followers may differ from the reference model in which the RL control policy was trained. DMSAC-RL uses an internal loop that adjusts the learned policy for the agents in the form of augmented input to solve the distributed control problem, including input-matched uncertainty parameters. We show that the synchronization error of the heterogeneous network is Uniformly Ultimately Bounded (UUB). Numerical analysis of a network of Multiple Input Multiple Output (MIMO) systems supports our theoretical findings.