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

Ben-ya Halevy, Yehudit Aperstein, Dotan Di Castro
6/23/2023
cs.ROcs.AIcs.LG

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Abstract

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.

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Cite this paper

@article{halevy2023offline,
  title  = {Offline Skill Graph (OSG): A Framework for Learning and Planning using Offline Reinforcement Learning Skills},
  author = {Ben-ya Halevy and Yehudit Aperstein and Dotan Di Castro},
  year   = {2023},
  eprint = {2306.13630},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.13630v1}
}

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