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In this paper, we investigate the problem of controlling probabilisticBoolean control networks (PBCNs) to achieve reachability with maximumprobability in the finite time horizon. We address three questions: 1) findingcontrol policies that achieve reachability with maximum probability underfixed, and particularly, varied finite time horizon, 2) leveraging priorknowledge to solve question 1) with faster convergence speed in scenarios wheretime is a variable framework, and 3) proposing an enhanced Q-learning (QL)method to efficiently address the aforementioned questions for large-scalePBCNs. For question 1), we demonstrate the applicability of QL method on thefinite-time reachability problem. For question 2), considering the possibilityof varied time frames, we incorporate transfer learning (TL) technique toleverage prior knowledge and enhance convergence speed. For question 3), anenhanced model-free QL approach that improves upon the traditional QL algorithmby introducing memory-efficient modifications to address these issues inlarge-scale PBCNs effectively. Finally, we apply the proposed method to twoexamples: a small-scale PBCN and a large-scale PBCN, demonstrating theeffectiveness of our approach.

Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks