There is a growing body of literature that formulates social human-robot interactions as sequential decision-making tasks. In such cases, reinforcement learning arises naturally since the interaction is a key component in both reinforcement learning and social robotics. This article surveys reinforcement learning approaches in social robotics. We propose a taxonomy that categorizes reinforcement learning methods in social robotics according to the nature of the reward function. We discuss the benefits and challenges of such methods and outline possible future directions.