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