Feedback stabilization of probabilistic finite state machines based on deep Q-network - Tian, Hui; Su, Xin; Hou, Yanfang (Frontiers in Computational Neuroscience, 2024)
This paper investigates the stabilization of probabilistic finite state machines (PFSMs) through the deep Q-network (DQN) technique, which is a model-free optimization method. First, the feedback stabilization problem of PFSMs is transformed into an optimization problem. Next, a necessary and sufficient stabilizability condition is derived for PFSMs. Then, under the stabilizability condition, the optimization problem can be solved by DQN. This method is particularly suitable for non-small-scale PFSMs. Finally, the effectiveness of our results is demonstrated through an example.