… We explore the potential of deep reinforcement learning to … as the training and trading
market environment. We train a … deep reinforcement learning approach to solve this problem. …

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

… In this paper, a deep reinforcement learning algorithm, namely Deep Deterministic Policy
Gradient (DDPG), to find the best trading strategy in the complex and dynamic stock market …

… deep reinforcement learning covering the mathematical concepts behind popular algorithms
as well as their practical … anyone looking to apply deep reinforcement learning in practice.” …

… Training approach for ameliorate the training efficiency and the mean-return, where the new
training approach … [114] employed a deterministic deep reinforcement learning approach …

… The reason that we focus on deep reinforcement learning is its high efficiency with which
we can solve some problems that are sometimes difficult. This technology includes all the …

… We are confident that this book will teach you the concepts of deep reinforcement learning
and not just how to narrowly code things in Python. If Python were to somehow disappear …

… We believe learning methods are capable of advancing this frontier, and have designed
two tasks that emphasize multi-modal control in different ways: 1) A narrow-clearance (0.5mm) …

… -based control methods have been … Reinforcement learning can online update its policy
through the observed data by interacting with the environment. Since a fast and stable learning …

… In Chapter 2, we introduce the field of machine learning and the deep learning approach. …
explain briefly where deep learning is situated in the broader field of machine learning. We …