Optimal Control of Logically Constrained Partially Observable and Multiagent Markov Decision Processes

• 成果类型: Article
• 署名作者: Kalagarla, Krishna C.; Kartik, Dhruva; Shen, Dongming; Jain, Rahul; Nayyar, Ashutosh; Nuzzo, Pierluigi
• 署名单位: University of Southern California; University of New Mexico; Amazon.com; Massachusetts Institute of Technology (MIT)
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3422213
• 发表日期: 2025
• 页码: 263-277
• 关键词: logic; PLANNING; robots; optimal control; Markov decision processes; Task analysis; Stochastic processes; Markov decision processes (MDPs); multiagent systems; partially observable Markov decision processes (POMDPs); stochastic optimal control; temporal logic

摘要

Autonomous systems often have logical constraints arising, for example, from safety, operational, or regulatory requirements. Such constraints can be expressed using temporal logic specifications. The system state is often partially observable. Moreover, it could encompass a team of multiple agents with a common objective but disparate information structures and constraints. In this article, we first introduce an optimal control theory for partially observable Markov decision processes with finite linear temporal logic constraints. We provide a structured methodology for synthesizing policies that maximize a cumulative reward while ensuring that the probability of satisfying a temporal logic constraint is sufficiently high. Our approach comes with guarantees on approximate reward optimality and constraint satisfaction. We then build on this approach to design an optimal control framework for logically constrained multiagent settings with information asymmetry. We illustrate the effectiveness of our approach by implementing it on several case studies.