no code implementations • 12 Dec 2023 • Hongyue Fan, Jingjie Ni, Fangfei Li
We address three questions: 1) finding control policies that achieve reachability with maximum probability under fixed, and particularly, varied finite time horizon, 2) leveraging prior knowledge to solve question 1) with faster convergence speed in scenarios where time is a variable framework, and 3) proposing an enhanced Q-learning (QL) method to efficiently address the aforementioned questions for large-scale PBCNs.
no code implementations • 29 Nov 2023 • Xianlun Peng, Yang Tang, Fangfei Li, Yang Liu
In this paper, we present a reinforcement learning (RL) method for solving optimal false data injection attack problems in probabilistic Boolean control networks (PBCNs) where the attacker lacks knowledge of the system model.
no code implementations • 11 Apr 2023 • Jingjie Ni, Fangfei Li
Then, to obtain the optimal policy, we propose QL, and fast small memory QL for large-scale systems.
no code implementations • 7 Apr 2023 • Jingjie Ni, Fangfei Li, Zheng-Guang Wu
In this paper, a deep reinforcement learning based method is proposed to obtain optimal policies for optimal infinite-horizon control of probabilistic Boolean control networks (PBCNs).