  Recommender systems (RSs) are becoming an inseparable part of our everyday
lives. They help us find our favorite items to purchase, our friends on social
networks, and our favorite movies to watch. Traditionally, the recommendation
problem was considered as a simple classification or prediction problem;
however, the sequential nature of the recommendation problem has been shown.
Accordingly, it can be formulated as a Markov decision process (MDP) and
reinforcement learning (RL) methods can be employed to solve it. In fact,
recent advances in combining deep learning with traditional RL methods, i.e.
deep reinforcement learning (DRL), has made it possible to apply RL to the
recommendation problem with massive state and action spaces. In this paper, a
survey on reinforcement learning based recommender systems (RLRSs) is
presented. We first recognize the fact that algorithms developed for RLRSs can
be generally classified into RL- and DRL-based methods. Then, we present these
RL- and DRL-based methods in a classified manner based on the specific RL
algorithm, e.g., Q-learning, SARSA, and REINFORCE, that is used to optimize the
recommendation policy. Furthermore, some tables are presented that contain
detailed information about the MDP formulation of these methods, as well as
about their evaluation schemes. Finally, we discuss important aspects and
challenges that can be addressed in the future.
