Authors:
Kenneth Schröder
;
Alexander Kastius
and
Rainer Schlosser
Affiliation:
Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
Keyword(s):
Reinforcement Learning, Markov Decision Problem, Conceptual Comparison, Recommendations.
Abstract:
Reinforcement Learning (RL) has continuously risen in popularity in recent years. Consequently, multiple RL algorithms and extensions have been developed for various use cases. This makes RL applicable to a wide range of problems today. When searching for suitable RL algorithms to specific problems, the options are overwhelming. Identifying the advantages and disadvantages of methods is difficult, as sources use conflicting terminology, imply improvements to alternative algorithms without mathematical or empirical proof, or provide incomplete information. As a result, there is the chance for engineers and researchers to miss alternatives or perfect-fit algorithms for their specific problems. In this paper, we identify and explain essential RL properties. Our discussion of different RL concepts allows to select, optimize, and compare RL algorithms and their extensions, as well as reason about their performance.