  The rapid changes in the finance industry due to the increasing amount of
data have revolutionized the techniques on data processing and data analysis
and brought new theoretical and computational challenges. In contrast to
classical stochastic control theory and other analytical approaches for solving
financial decision-making problems that heavily reply on model assumptions, new
developments from reinforcement learning (RL) are able to make full use of the
large amount of financial data with fewer model assumptions and to improve
decisions in complex financial environments. This survey paper aims to review
the recent developments and use of RL approaches in finance. We give an
introduction to Markov decision processes, which is the setting for many of the
commonly used RL approaches. Various algorithms are then introduced with a
focus on value and policy based methods that do not require any model
assumptions. Connections are made with neural networks to extend the framework
to encompass deep RL algorithms. Our survey concludes by discussing the
application of these RL algorithms in a variety of decision-making problems in
finance, including optimal execution, portfolio optimization, option pricing
and hedging, market making, smart order routing, and robo-advising.
