Automated Reinforcement Learning: An Overview

Understand

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 consideration, 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, such as combinatorial optimization, where researchers and system designers are not necessarily RL experts.
• Besides, many modeling decisions are typically made manually, such as defining state and action space, size of batches, batch update frequency, and time steps.