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.

中文翻译：

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社交机器人中的强化学习方法

越来越多的文献将社会人机交互表述为顺序决策任务。在这种情况下，强化学习自然而然地出现，因为交互是强化学习和社交机器人的关键组成部分。本文调查了社交机器人中的强化学习方法。我们提出了一种分类法，根据奖励函数的性质对社交机器人中的强化学习方法进行分类。我们讨论了这些方法的好处和挑战，并概述了未来可能的方向。