  Fairness-aware learning aims at satisfying various fairness constraints in
addition to the usual performance criteria via data-driven machine learning
techniques. Most of the research in fairness-aware learning employs the setting
of fair-supervised learning. However, many dynamic real-world applications can
be better modeled using sequential decision-making problems and fair
reinforcement learning provides a more suitable alternative for addressing
these problems. In this article, we provide an extensive overview of fairness
approaches that have been implemented via a reinforcement learning (RL)
framework. We discuss various practical applications in which RL methods have
been applied to achieve a fair solution with high accuracy. We further include
various facets of the theory of fair reinforcement learning, organizing them
into single-agent RL, multi-agent RL, long-term fairness via RL, and offline
learning. Moreover, we highlight a few major issues to explore in order to
advance the field of fair-RL, namely - i) correcting societal biases, ii)
feasibility of group fairness or individual fairness, and iii) explainability
in RL. Our work is beneficial for both researchers and practitioners as we
discuss articles providing mathematical guarantees as well as articles with
empirical studies on real-world problems.
