… We adopt a reinforcement learning (RL) approach to learn a policy resulting a highly accurate
… design an active learning algorithm as a policy based on deep reinforcement learning. We …

… [3] by combining deep learning and reinforcement learning [2]. … the success in deep
reinforcement learning can be applied … are continuous and reinforcement learning for continuous …

… These feature are exploited by various techniques starting from traditional statistical methods,
passing by neural networks and deep learning, to deep reinforcement learning. …

… achievements of deep reinforcement learning (RL). … learning, deep learning and reinforcement
learning. Next we discuss core RL elements, including value function, in particular, Deep …

… version AlphaGo Zero, is deep reinforcement learning (DRL), which is an improved version
of reinforcement learning (RL) with the integration of deep learning. Once well trained, an RL/…

… developments in reinforcement learning and deep learning to develop a novel adaptive,
model-free controller for general discrete-time processes. The deep reinforcement learning (…

… reinforcement learning approaches have difficulties in the choice of market features. Deep
learning approaches … The combination of RL and DL, called deep reinforcement learning (DRL…

… This work examines applying deep reinforcement learning to a chemical production …
the differing approaches. Results show that the reinforcement learning method outperforms …

… as the deep reinforcement learning algorithm involved in the approaches summarized in …
Deep reinforcement learning and the algorithms used in the research involved in this review …

… Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for
UAV control and present a novel and highly energyefficient DRL-based method, which we call …