We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

… Many papers have reported the application of deep reinforcement learning in power systems, …
Deep reinforcement learning combines the perception function of deep learning with the …

… in deep reinforcement learning can be applied on process control problems. In process control,
action spaces are continuous and reinforcement learning for … on non-linear systems. We …

… The main issue was instability; whereas in tabular and linear systems, RL reliably moved …
, with the report of the Deep Q Network (DQN), the first deep RL system that learned to play …

… in recommender systems. He received his BS degree from the Department of Mathematics,
East China Normal University in July 2016. Yanhua also contributed to some open-source …

… and deep neural networks, which allows them to harness the information in large and rich
datasets. Deep reinforcement learning combines deep learning … bonus even in a linear system. …

… Furthermore, DRL controller does not require parameter tuning or real-time optimization,
hence it is capable of controlling highly non-linear systems and high frequency control tasks. In …

… develop deep reinforcement learning approaches for the control of nonlinear systems with
… The method converts the GHJB equation to a set of nonlinear equations. The linearization of …

… of machine learning through TensorFlow. TensorFlow is Google’s new software library for
deep learning … will need to understand how machine learning systems learn, and will need to …

… In the present work, we follow this rationale by considering a reinforcement learning (RL) …
aim at obtaining the CARE starting from the HJB equation rewritten for the linear system as …