国家(计算机科学)
符号
布尔模型
概率逻辑
强化学习
数学
计算机科学
离散数学
算法
理论计算机科学
人工智能
算术
作者
Yang Liu,Zejiao Liu,Amol Yerudkar,Carmen Del Vecchio
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-8
标识
DOI:10.1109/tac.2023.3327618
摘要
In this paper, the state-flipped control technique is explored to investigate the stabilization of probabilistic Boolean networks (PBNs). Changing the values of many nodes from 0 to 1 (or from 1 to 0) is called the state-flipped control. The concepts of fixed point, reachable sets, and finite-time global stabilization of PBNs under state-flipped control are proposed. Several necessary and sufficient conditions for global stabilization are also derived based on the reachable sets of a given state. Furthermore, a model-free reinforcement learning (RL) algorithm, namely $Q$ -learning ( $Q$ L), is presented to design a flip sequence for any state that steers the state to a given destination state, thereby achieving finite-time global stabilization via state-flipped control. In addition, the process of finding the minimum flip set is proposed under the semi-tensor product and $Q$ L methods. Finally, the viability of the results in the paper is shown by considering a 12-gene hepatocellular cancer cell tumor network.
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