On periodic reference tracking using batch-mode reinforcement learning with application to gene regulatory network control
On periodic reference tracking using batch-mode reinforcement learning with application to gene regulatory network control
复制标题
使用批处理模式强化学习进行周期性参考跟踪及其在基因调控网络控制中的应用
DOI：
10.1109/cdc.2013.6760515
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
G. Stan
中科院分区：
文献类型：
--
作者：
Aivar Sootla;N. Strelkowa;D. Ernst;Mauricio Barahona;G. Stan
在本文中，我们考虑的周期性参考跟踪问题的批量模式强化学习的框架，研究方法解决最优控制问题的唯一知识的一组轨迹。特别是，我们扩展了现有的批处理模式强化学习算法，称为拟合Q迭代，周期性参考跟踪问题。所提出的周期性参考跟踪算法明确地利用了参考轨迹的未来值及其周期性的先验知识。我们讨论了我们的方法的属性，并说明它的参考跟踪的问题，被称为广义represilator的合成生物学基因调控网络。该系统可以产生衰减但寿命长的振荡，这使得它成为跟踪问题的一个有趣的应用。
In this paper, we consider the periodic reference tracking 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 extend 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 on the 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 application for the tracking problem.