Random Forest Q-Learning for Feedback Stabilization of Probabilistic Boolean Control Networks

Abstract

In this paper, we propose a novel random forest (RF) Q-learning hybrid with experience replay for feedback stabilization of probabilistic Boolean control networks (PBCNs). In particular, by resorting to a model-free reinforcement learning (RL) framework, we present a random forest Q-learning (QLRF) algorithm to design optimal state feedback controllers, thereby stabilizing PBCNs to a given equilibrium point. In reference to better the process of learning the Q-table by replacing it with a function approximator, we substitute the existent neural network (NN) architecture by a RF. We provide insights on the overall computational complexity between the two ways of solving the same problem, proving RF better than its NN counterparts for such applications. The simulations performed on some of the standard examples in the literature demonstrates the effectiveness of the proposed idea.