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Reinforcement Learning has received wide interest due to its success incompetitive games. Yet, its adoption in everyday applications is limited (e.g.industrial, home, healthcare, etc.). In this paper, we address this limitationby presenting a framework for planning over offline skills and solving complextasks in real-world environments. Our framework is comprised of three modulesthat together enable the agent to learn from previously collected data andgeneralize over it to solve long-horizon tasks. We demonstrate our approach bytesting it on a robotic arm that is required to solve complex tasks.

Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills