2013/03/12 by Aivar Sootla, Sootla, Aivar, Natalja Strelkowa +7 · 1 citation
Biochemistry, Genetics and Molecular Biology · #CRISPR and Genetic Engineering #FOS: Electrical engineering #FOS: Mathematics #Gene Regulatory Network Analysis #Optimization and Control (math.OC) #Systems and Control (eess.SY) #Viral Infectious Diseases and Gene Expression in Insects #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1303.2987
openalex publication_date 2013/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we consider the periodic reference tracking problem in the\nframework of batch-mode reinforcement learning, which studies methods for\nsolving optimal control problems from the sole knowledge of a set of\ntrajectories. In particular, we extend an existing batch-mode reinforcement\nlearning algorithm, known as Fitted Q Iteration, to the periodic reference\ntracking problem. The presented periodic reference tracking algorithm\nexplicitly exploits a priori knowledge of the future values of the reference\ntrajectory and its periodicity. We discuss the properties of our approach and\nillustrate it on the problem of reference tracking for a synthetic biology gene\nregulatory network known as the generalised repressilator. This system can\nproduce decaying but long-lived oscillations, which makes it an interesting\nsystem for the tracking problem. In our companion paper we also take a look at\nthe regulation problem of the toggle switch system, where the main goal is to\ndrive the system's states to a specific bounded region in the state space.\n