Temporal-difference Q-learning in active fault diagnosis

The paper deals with a novel design of an approximate active fault detector for discrete-time stochastic linear Markovian switching systems on the infinite-time horizon. The problem is formulated as an optimization problem with the aim to minimize a general discounted detection cost criterion. The proposed solution is inspired by approximate dynamic programming and reinforcement learning. The active fault detector is trained by a temporal-difference Q-learning algorithm with a linear parametric Q-function approximation adjusted to fit the true Q-function. The main advantage is that this approach is computationally less expensive than a temporal-difference learning with a value function.