  We discuss deep reinforcement learning in an overview style. We draw a big
picture, filled with details. We discuss six core elements, six important
mechanisms, and twelve applications, focusing on contemporary work, and in
historical contexts. We start with background of artificial intelligence,
machine learning, deep learning, and reinforcement learning (RL), with
resources. Next we discuss RL core elements, including value function, policy,
reward, model, exploration vs. exploitation, and representation. Then we
discuss important mechanisms for RL, including attention and memory,
unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and
learning to learn. After that, we discuss RL applications, including games,
robotics, natural language processing (NLP), computer vision, finance, business
management, healthcare, education, energy, transportation, computer systems,
and, science, engineering, and art. Finally we summarize briefly, discuss
challenges and opportunities, and close with an epilogue.
