  This article is a gentle discussion about the field of reinforcement learning
in practice, about opportunities and challenges, touching a broad range of
topics, with perspectives and without technical details. The article is based
on both historical and recent research papers, surveys, tutorials, talks,
blogs, books, (panel) discussions, and workshops/conferences. Various groups of
readers, like researchers, engineers, students, managers, investors, officers,
and people wanting to know more about the field, may find the article
interesting.
  In this article, we first give a brief introduction to reinforcement learning
(RL), and its relationship with deep learning, machine learning and AI. Then we
discuss opportunities of RL, in particular, products and services, games,
bandits, recommender systems, robotics, transportation, finance and economics,
healthcare, education, combinatorial optimization, computer systems, and
science and engineering. Then we discuss challenges, in particular, 1)
foundation, 2) representation, 3) reward, 4) exploration, 5) model, simulation,
planning, and benchmarks, 6) off-policy/offline learning, 7) learning to learn
a.k.a. meta-learning, 8) explainability and interpretability, 9) constraints,
10) software development and deployment, 11) business perspectives, and 12)
more challenges. We conclude with a discussion, attempting to answer: "Why has
RL not been widely adopted in practice yet?" and "When is RL helpful?".
