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

… , deep learning is enabling reinforcement learning (RL) to scale to problems that were previously
intractable, such as learning … cover central algorithms in deep RL, including the deep Q-…

… Deep reinforcement learning is the combination of reinforcement learning (RL) and deep
learning. This field of research has been able to solve a wide range of complex decisionmaking …

… Deep Reinforcement Learning. By combining the concepts from deep learning and Reinforcement
Learning, … research in the field of Deep Reinforcement Learning, this book also covers …

In recent years, significant progress has been made in solving challenging problems across
various domains using deep reinforcement learning (RL). Reproducing existing work and …

… deep reinforcement learning. Note that this review is intended for beginners in exploration for
deep reinforcement learning; … the paper, we will use ’reinforcement learning’ as it is a more …

… This kind of task requires the so-called deep reinforcement learning (DRL), which is an indepth
combination of artificial neural network (ANN) and reinforcement learning (RL). The “deep…

… This book provides the most reliable entry to deep reinforcement learning, bridging the gap
… is a book for pure fans of reinforcement learning, in particular deep reinforcement learning. …

… -making ability of reinforcement learning so that it … , deep learning has stimulated the further
development of many subfields of reinforcement learning, such as hierarchical reinforcement …

… techniques can be combined with reinforcement learning methods to learn useful … recent
advances in deep reinforcement learning with focus on the most used deep architectures such …