A Formal Language for Performance Evaluation Based on Reinforcement Learning

Abstract

Temporal Logics are a rich variety of logical systems designed for specifying properties over time, and about events and changes in the world over time. Traditional temporal logic, however, is limited to binary outcomes true or false and lacks the capacity to specify performance properties of a system such as the maximum, minimum, or average costs between states. Current languages do not accommodate the quantification of such performance properties, especially in scenarios involving infinite execution paths where performance property like cumulative sums may fail to converge. To this end, this paper introduces a novel formal language aimed at assessing system performance, which encapsulates not only temporal dynamics but also various performance-related properties. In this study, this paper utilizes reinforcement learning techniques to compute the values of performance property formulas. Finally, in the experimental part, a formal language representation of system performance properties was implemented, and the values of the performance property formulas were computed using reinforcement learning. The effectiveness and feasibility of the proposed method were validated.