Approximate Information States for Worst Case Control and Learning in Uncertain Systems

• 成果类型: Article
• 署名作者: Dave, Aditya; Nishanth Venkatesh, S.; Malikopoulos, Andreas A.
• 署名单位: Cornell University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3422889
• 发表日期: 2025
• 关键词: Discrete-time; MINIMAX CONTROL; COMMUNICATION; optimality; FRAMEWORK

摘要

In this article, we investigate discrete-time decision-making problems in uncertain systems with partially observed states. We consider a nonstochastic model, where uncontrolled disturbances acting on the system take values in bounded sets with unknown distributions. We present a general framework for decision-making in such problems by using the notion of the information state and approximate information state and introduce conditions to identify an uncertain variable that can be used to compute an optimal strategy through a dynamic program (DP). Next, we relax these conditions and define approximate information states that can be learned from output data without knowledge of system dynamics. We use approximate information states to formulate a DP that yields a strategy with a bounded performance loss. Finally, we illustrate the application of our results in control and reinforcement learning using numerical examples.