  Reinforcement Learning and recently Deep Reinforcement Learning are popular
methods for solving sequential decision making problems modeled as Markov
Decision Processes. RL modeling of a problem and selecting algorithms and
hyper-parameters require careful considerations as different configurations may
entail completely different performances. These considerations are mainly the
task of RL experts; however, RL is progressively becoming popular in other
fields where the researchers and system designers are not RL experts. Besides,
many modeling decisions, such as defining state and action space, size of
batches and frequency of batch updating, and number of timesteps are typically
made manually. For these reasons, automating different components of RL
framework is of great importance and it has attracted much attention in recent
years. Automated RL provides a framework in which different components of RL
including MDP modeling, algorithm selection and hyper-parameter optimization
are modeled and defined automatically. In this article, we explore the
literature and present recent work that can be used in automated RL. Moreover,
we discuss the challenges, open questions and research directions in AutoRL.
