A Survey of Deep Reinforcement Learning Approaches in Robotic Manipulation

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Neural-network-based reinforcement learning has driven a broad set of breakthroughs across many application areas. In the specific case of robotic manipulation, these methods raise the prospect of machines acquiring dexterous, human-like manipulation skills straight from visual input. This survey examines where reinforcement learning algorithms currently stand within this field. It lays out the core theory and terminology involved, and highlights the principal obstacles that still restrict these algorithms from being deployed on real-world robotic problems. The paper closes with the authors' outlook on promising directions for continued research.