Self-triggered control of probabilistic Boolean control networks: A reinforcement learning approach

In this work, strategies to devise an optimal feedback control of probabilistic Boolean control networks (PBCNs) are discussed. Reinforcement learning (RL) based control is explored in order to minimize model design efforts and regulate PBCNs with high complexities. A Q-learning random forest ( QLRF) algorithm is proposed; by making use of the algorithm, state feedback controllers are designed to stabilize the PBCNs at a given equilibrium point. Further, by adopting QLRF stabilized closed-loop PBCNs, a Lyapunov function is defined, and a method to construct the same is presented. By utilizing such Lyapunov functions, a novel self-triggered control (STC) strategy is proposed, whereby the controller is recomputed according to a triggering schedule, resulting in an optimal control strategy while retaining the closed-loop PBCN stability. Finally, the results are verified using computer simulations. (c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This work discusses the optimal feedback control strategies for probabilistic Boolean control networks (PBCNs). Reinforcement learning (RL) is explored to minimize the model design efforts and regulate PBCNs with high complexities. A Q-learning random forest (QLRF) algorithm is proposed to design state feedback controllers for stabilizing the PBCNs at a given equilibrium point. Furthermore, a Lyapunov function is defined by adopting QLRF-stabilized closed-loop PBCNs, and a method to construct the same is presented. A novel self-triggered control (STC) strategy is proposed by utilizing such Lyapunov functions, where the controller is recomputed according to a triggering schedule, resulting in an optimal control strategy while maintaining the stability of the closed-loop PBCN. The results are verified through computer simulations.