Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated robotic behaviors. Robotics problems, however, pose fundamental difficulties for the application of RL, stemming from the complexity and cost of interacting with the physical world. This article provides a modern survey of DRL for robotics, with a particular focus on evaluating the real-world successes achieved with DRL in realizing several key robotic competencies. Our analysis aims to identify the key factors underlying those exciting successes, reveal underexplored areas, and provide an overall characterization of the status of DRL in robotics. We highlight several important avenues for future work, emphasizing the need for stable and sample-efficient real-world RL paradigms, holistic approaches for discovering and integrating various competencies to tackle complex long-horizon, open-world tasks, and principled development and evaluation procedures. This survey is designed to offer insights for both RL practitioners and roboticists toward harnessing RL's power to create generally capable real-world robotic systems.

翻译：强化学习（RL），特别是其与深度神经网络的结合（称为深度强化学习，DRL），已在广泛的应用中展现出巨大潜力，这表明其在实现复杂机器人行为方面具有潜力。然而，机器人学问题为RL的应用带来了根本性困难，这源于与物理世界交互的复杂性和成本。本文对机器人学中的DRL进行了现代综述，特别侧重于评估DRL在实现若干关键机器人能力方面所取得的现实世界成功。我们的分析旨在识别那些令人兴奋的成功背后的关键因素，揭示尚未充分探索的领域，并提供对机器人学中DRL现状的整体描述。我们强调了未来工作的几个重要方向，包括对稳定且样本高效的现实世界RL范式的需求、发现和整合各种能力以应对复杂长时域开放世界任务的整体方法，以及原则性的开发和评估流程。本综述旨在为RL从业者和机器人学家提供见解，以利用RL的力量创建具备通用能力的现实世界机器人系统。