… for a multi-unit series system … deep reinforcement learning (DRL) algorithm for the semi-Markov
decision processes (SMDP) to find an opportunistic CBM policy for a multi-unit system …

… By employing a customized deep reinforcement learning algorithm, … approach differs from
previous research on maintenance optimization for balanced systems, the proposed method …

… applied to a system with four parallel units. In the second study, we delve into Deep
Reinforcement Learning, developing a framework designed for multi-unit systems experiencing …

… of the multi-component system. This article presents an … systems with load sharing, solved
by a modified proximal policy optimization approach based on deep reinforcement learning …

… generally try to minimize maintenance costs for multi-unit systems by developing …
Reinforcement learning methods as you will see in the coming chapters use a synergistic approach …

… In this work we proposed an curriculum learning method based on interfering the
opponent to solve an heterogeneous force combat. According to the empirical results, the two …

… policies for simplified multi-unit systems, which impedes those methods from be generalized
to … model of the system and derives a PM policy for a two-machine-one-buffer manufacturing …

… system using a reinforcement learning approach. The system utilises a critical machine with
… for spares and imperfect maintenance based on the remaining life of multi-unit systems. For …

… Multi-unit residential buildings … is called deep reinforcement learning (DRL), which comes
to handling large state spaces [33]. The DRL is a category of advanced machine learning …

… to the challenge caused by convergence in the multi-unit power grid. … deep reinforcement
learning algorithm termed as the imitation guided-exploration multi-agent twin-delayed deep …