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@inproceedings{Sootla:2012,
author = {Sootla, A and Strelkowa, N and Ernst, D and Barahona, M and Stan, G-B},
title = {On Periodic Reference Tracking Using Batch-Mode Reinforcement Learning with Application to Gene Regulatory Network Control},
year = {2012}
}
TY - CPAPER
AB - In this paper, we consider the periodic referencetracking problem in the framework of batch-mode reinforcement learning, which studies methods for solving optimal control problems from the sole knowledge of a set of trajectories. In particular, we adapt an existing batch-mode reinforcement learning algorithm, known as Fitted Q iteration, to the periodic reference tracking problem. The presented periodic reference tracking algorithm explicitly exploits a priori knowledge of the future values of the reference trajectory and its periodicity. We discuss the properties of our approach and illustrate it onthe problem of reference tracking for a synthetic biology gene regulatory network known as the generalised repressilator. This system can produce decaying but long-lived oscillations, which makes it an interesting system for the tracking problem. In our companion paper submitted to this conference we also take a look at the regulation problem of the toggle switch system, where the main goal is to drive the systems states to a specific bounded region in the state space.
AU - Sootla,A
AU - Strelkowa,N
AU - Ernst,D
AU - Barahona,M
AU - Stan,G-B
PY - 2012///
TI - On Periodic Reference Tracking Using Batch-Mode Reinforcement Learning with Application to Gene Regulatory Network Control
ER -