Who to Blame? learning and control strategies with information asymmetry
Who to Blame? learning and control strategies with information asymmetry
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该怪谁？
DOI：
10.1109/acc.2016.7526122
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
M. Tomizuka
中科院分区：
文献类型：
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作者：
Changliu Liu;Wenlong Zhang;M. Tomizuka
机器人-机器人交互（RRI）的兴起推动了新型控制器设计技术的发展。机器人不应该使用固定的控制律，而应该选择使设计者指定的某些成本函数最小化的动作。然而，由于一个机器人的成本函数可能不知道其他机器人（信息不对称），需要特殊的推理策略，多个机器人学习合作。分析表明，传统的学习和控制策略可能会导致不稳定的多智能体系统，因为没有考虑其他智能体的不完善性。本文提出了一种新的学习和控制策略，处理不完美的代理之间的相互作用。分析和仿真结果表明，该策略提高了系统的性能。
The rise of robot-robot interactions (RRI) is pushing for novel controller design techniques. Instead of using fixed control laws, robots should choose actions to minimize some cost functions specified by the designer. However, since the cost function of one robot may not be known to other robots (information asymmetry), special reasoning strategies are needed for multiple robots to learn to cooperate. Analysis shows that conventional learning and control strategies can lead to instability in a multi-agent system since the imperfection of other agents is not considered. In this paper, a new learning and control strategy that deals with interactions among imperfect agents is proposed. Analysis and simulation results show that the proposed strategy improves the performance of the system.