Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills
This addresses the problem of applying reinforcement learning to everyday domains like robotics, but appears incremental as it builds on existing offline RL methods.
The paper tackles the limited adoption of reinforcement learning in real-world applications by introducing a framework for planning with offline skills to solve complex tasks, demonstrating it on a robotic arm.
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.