Human-Centered Reinforcement Learning: A Survey

Abstract

Human-centered reinforcement learning (RL), in which an agent learns how to perform a task from evaluative feedback delivered by a human observer, has become more and more popular in recent years. The advantage of being able to learn from human feedback for a RL agent has led to increasing applicability to real-life problems. This paper describes the state-of-the-art human centered RL algorithms and aims to become a starting point for researchers who are initiating their endeavors in human-centered RL. Moreover, the objective of this paper is to present a comprehensive survey of the recent breakthroughs in this field and provide references to the most interesting and successful works. After starting with an introduction of the concepts of RL from environmental reward, this paper discusses the origins of human-centered RL and its difference from traditional RL. Then we describe different interpretations of human evaluative feedback, which have produced many human-centered RL algorithms in the past decade. In addition, we describe research on agents learning from both human evaluative feedback and environmental rewards as well as on improving the efficiency of human-centered RL. Finally, we conclude with an overview of application areas and a discussion of future work and open questions.