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Deep Reinforcement Learning (DRL) is increasingly applied in cyber–physical systems for automation tasks. It is important to record the developing trends in DRL’s applications to help researchers overcome common problems using common solutions. This survey investigates trends seen within two applied settings: motor control tasks, and resource allocation tasks. The common problems include intractability of the action space, or state space, as well as hurdles associated with the prohibitive cost of training systems from scratch in the real-world. Real-world training data is sparse and difficult to derive and training in real-world can damage real-world learning systems. Researchers have provided a set of common as well as unique solutions. Tackling the problem of intractability, researchers have succeeded in guiding network training with handcrafted reward functions, auxiliary learning, and by simplifying the state …