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

arXiv: https://arxiv.org/abs/2306.13630 · HTML: https://mlsanity.com/paper/2306.13630/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Placed in

- Offline RL and imitation learning from data / [Offline RL algorithms](https://mlsanity.com/offlinerl/offline-rl-algorithms/)

## Builds on (most cited first, 9 of 9)

- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning](https://mlsanity.com/paper/1910.11956/) (2019)
- [Dynamics-Aware Unsupervised Discovery of Skills](https://mlsanity.com/paper/1907.01657/) (2019)
- [COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning](https://mlsanity.com/paper/2010.14500/) (2020)
- [Search-Based Task Planning with Learned Skill Effect Models for Lifelong Robotic Manipulation](https://mlsanity.com/paper/2109.08771/) (2021)
- [Finding Options that Minimize Planning Time](https://mlsanity.com/paper/1810.07311/) (2018)
- [Constructing Abstraction Hierarchies Using a Skill-Symbol Loop](https://mlsanity.com/paper/1509.07582/) (2015)

## Built on by (most cited first, 1 of 1)

- [Behaviour Discovery and Attribution for Explainable Reinforcement Learning](https://mlsanity.com/paper/2503.14973/) (2025)

## Abstract (arXiv)

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.