基于尺度策略迭代的未知离散线性系统强化学习

IF 5.9 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Zhen Pang , Shengda Tang , Jun Cheng , Shuping He
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引用次数: 0

摘要

在最优控制问题中,策略迭代(PI)是用于线性系统最优控制器设计的一种强大的强化学习(RL)工具。然而,对初始稳定控制政策的需要极大地限制了其适用性。为了解决这一限制,本文提出了一种新的缩放技术,该技术逐步使一系列稳定的缩放系统更接近原始系统,从而能够获得稳定的控制增益。基于所设计的尺度更新律,我们开发了基于模型和无模型的尺度策略迭代(SPI)算法,用于解决已知和完全未知系统动力学场景下离散时间线性系统的最优控制问题。与现有基于PI的RL工作不同,SPI算法不需要初始稳定增益来初始化算法,它们可以在任何初始控制增益下实现最优控制。最后,数值结果验证了理论结果,验证了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scaling policy iteration based reinforcement learning for unknown discrete-time linear systems
In optimal control problem, policy iteration (PI) is a powerful reinforcement learning (RL) tool used for designing optimal controller for the linear systems. However, the need for an initial stabilizing control policy significantly limits its applicability. To address this constraint, this paper proposes a novel scaling technique, which progressively brings a sequence of stable scaled systems closer to the original system, enabling the acquisition of stable control gain. Based on the designed scaling update law, we develop model-based and model-free scaling policy iteration (SPI) algorithms for solving the optimal control problem for discrete-time linear systems, in both known and completely unknown system dynamics scenarios. Unlike existing works on PI based RL, the SPI algorithms do not necessitate an initial stabilizing gain to initialize the algorithms, they can achieve the optimal control under any initial control gain. Finally, the numerical results validate the theoretical findings and confirm the effectiveness of the algorithms.
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来源期刊
Automatica
Automatica 工程技术-工程:电子与电气
CiteScore
10.70
自引率
7.80%
发文量
617
审稿时长
5 months
期刊介绍: Automatica is a leading archival publication in the field of systems and control. The field encompasses today a broad set of areas and topics, and is thriving not only within itself but also in terms of its impact on other fields, such as communications, computers, biology, energy and economics. Since its inception in 1963, Automatica has kept abreast with the evolution of the field over the years, and has emerged as a leading publication driving the trends in the field. After being founded in 1963, Automatica became a journal of the International Federation of Automatic Control (IFAC) in 1969. It features a characteristic blend of theoretical and applied papers of archival, lasting value, reporting cutting edge research results by authors across the globe. It features articles in distinct categories, including regular, brief and survey papers, technical communiqués, correspondence items, as well as reviews on published books of interest to the readership. It occasionally publishes special issues on emerging new topics or established mature topics of interest to a broad audience. Automatica solicits original high-quality contributions in all the categories listed above, and in all areas of systems and control interpreted in a broad sense and evolving constantly. They may be submitted directly to a subject editor or to the Editor-in-Chief if not sure about the subject area. Editorial procedures in place assure careful, fair, and prompt handling of all submitted articles. Accepted papers appear in the journal in the shortest time feasible given production time constraints.
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